Transition Cow Monitoring: Diagnostic Approaches for Subclinical Disease

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-11

Key Takeaways

• Subclinical hyperketonemia and hypocalcemia are prevalent postpartum metabolic disorders in dairy cows, significantly impacting milk yield and fertility, necessitating targeted diagnostic approaches. Blood beta-hydroxybutyrate (BHB) point-of-care meters are primary tools for hyperketonemia, with thresholds typically ≥ 1.2 mmol/L, while subclinical hypocalcemia is assessed via blood total calcium (< 2.0 mmol/L) or ionized calcium (< 1.0 mmol/L) within the first 48 hours postpartum.
• Milk Fourier-transform infrared (FTIR) spectroscopy offers a cost-effective method for herd-level hyperketonemia surveillance, providing prevalence estimates, but lacks the sensitivity and specificity for individual cow diagnosis, requiring confirmatory blood BHB testing. Milk ketone concentrations correlate moderately with blood levels and lag by several hours, making them better suited for screening than precise individual diagnosis.
• The timing of sample collection is critical: blood BHB is optimally tested between days 5-7 postpartum to capture peak incidence, while calcium assessment must occur within the first 24-48 hours postpartum to accurately reflect nadir concentrations. Sampling outside these windows can lead to misclassification of disease status.
• Subclinical metritis diagnosis relies on a combination of vaginal discharge scoring (≥ 2 on a 0-3 scale) and rectal palpation findings, as there is no single definitive biomarker; systemic parameters like rectal temperature (> 39.5°C) are supportive but not solely diagnostic.
• Diagnostic reasoning must differentiate between herd-level surveillance (estimating prevalence for management adjustments, tolerating lower test sensitivity) and cow-level diagnosis (classifying individual animals, requiring higher test specificity and predictive value).
• Predictive models utilizing milk FTIR and genomic data can estimate herd prevalence but are not reliable for individual cow diagnosis, necessitating confirmatory testing. Integrating multiple data streams, including milk production and activity monitors, can enhance monitoring efficiency but requires farm-specific validation.

The transition period, generally defined as the three weeks before and three weeks after calving, carries the highest risk of metabolic and infectious disease in the dairy cow's production cycle. Subclinical disorders, particularly hyperketonemia, subclinical hypocalcemia, and subclinical endometritis, escape routine observation yet measurably reduce milk yield, impair fertility, and increase culling risk. This article provides a diagnostic framework for practicing veterinarians designing fresh cow monitoring programs, with emphasis on test selection, sampling timing, interpretation thresholds, and herd-level versus cow-level decision logic. It addresses the question of how to detect disease before clinical signs appear, using tools ranging from point-of-care meters to milk Fourier-transform infrared (FTIR) spectroscopy and predictive models.

The reader is assumed to be a veterinarian familiar with dairy production medicine who seeks a structured approach to subclinical disease surveillance. Treatment protocols are excluded, the focus rests entirely on detection, classification, and interpretation. Where the evidence base is contested or regionally variable, this is stated explicitly.

At a Glance

Physiology of the Transition Period and Subclinical Disease

The periparturient cow undergoes a rapid shift from gestation to lactation, with glucose demand rising sharply as the mammary gland begins synthesizing lactose. Hepatic gluconeogenesis must accelerate within days, and lipid mobilization from adipose tissue supplies nonesterified fatty acids (NEFA) as an alternative fuel. When hepatic oxidation of NEFA is incomplete, ketone bodies, principally beta-hydroxybutyrate (BHB), accumulate in blood. This metabolic adaptation is physiological to a degree, but it becomes pathological when ketone production exceeds tissue utilization.

Subclinical hyperketonemia is defined by elevated blood BHB without clinical signs such as inappetence, dullness, or neurologic abnormality. The condition is common in early lactation and is associated with increased risk of displaced abomasum, clinical ketosis, and reduced milk production. The diagnostic challenge is that affected cows appear normal at the time of testing, which makes targeted surveillance necessary instead of opportunistic.

Calcium homeostasis presents a parallel problem. The onset of lactation demands a sudden outflow of calcium into colostrum and milk, and cows that cannot mobilize skeletal reserves rapidly enough develop hypocalcemia. Subclinical hypocalcemia, defined by low blood calcium without recumbency or paresis, is more prevalent than the clinical form and has been associated with retained placenta, metritis, and reduced rumen motility. Diagnosis requires blood sampling within the first days after calving, as calcium concentrations normally recover within 48 to 72 hours.

Test Modalities and Their Operating Characteriztics

Blood Beta-Hydroxybutyrate Measurement

Point-of-care handheld meters that measure BHB from whole blood are the most widely used tools for subclinical ketosis detection in practice. These devices provide results within seconds and require minimal laboratory infrastructure. Their accuracy has been evaluated against laboratory reference methods, and most commercially available meters perform acceptably for clinical decision-making. However, meter performance varies between manufacturers and between batches of test strips, so periodic validation against a laboratory method is prudent.

The choice of threshold for defining hyperketonemia materially affects test interpretation. Different published classification systems use blood BHB cut-points that range from 1.0 to 1.4 mmol/L. The body publishing the classification should be named when a threshold is applied in practice, because the prevalence estimate and the resulting management decision depend on that choice. A cow-side test with high sensitivity will identify more candidates for confirmatory testing, while a higher threshold reduces false positives at the cost of missed cases.

Milk Ketone Testing

Milk BHB and acetone can be measured using test strips, dipsticks, or FTIR spectroscopy at routine milk recording. Milk ketone concentrations correlate with blood concentrations, but the correlation is moderate instead of strong, and milk levels lag blood levels by several hours. Milk testing is therefore better suited to herd-level surveillance than to individual cow diagnosis. Monthly milk recording data can identify cows with elevated milk ketones, but the interval between samples means that transient elevations may be missed.

Milk Fourier-Transform Infrared Spectroscopy

Milk FTIR spectroscopy generates a spectral fingerprint of milk composition that can be used to predict ketone body concentrations without additional wet chemistry. The approach has been incorporated into routine milk recording schemes in several countries, providing a low-cost method for herd-level hyperketonemia monitoring. Predictive models built on FTIR data have demonstrated sufficient accuracy to estimate monthly herd prevalence, but they lack the sensitivity and specificity required for individual cow diagnostics. Artificial neural network models using FTIR data and milk composition variables have achieved reported sensitivity and specificity near 80 percent for individual cow classification, yet this performance remains below what is acceptable for clinical diagnosis in a single animal. The distinction between herd surveillance and individual diagnosis is central to using FTIR data correctly.

Herd-Level Versus Cow-Level Diagnostic Reasoning

The purpose of testing determines the required test performance. Herd-level monitoring aims to estimate the prevalence of subclinical disease so that nutritional management can be adjusted. For this purpose, a test with moderate sensitivity and specificity can be acceptable, because errors at the individual level average out across the herd. Monthly prevalence estimates from milk FTIR or from a sample of blood tests can guide ration formulation, dry cow management, and transition cow protocols.

Cow-level diagnosis, by contrast, requires that the test correctly classify each individual animal. A false positive leads to unnecessary treatment and misallocated labor, while a false negative leaves disease undetected. The predictive value of a positive test depends on the prevalence of disease in the population being tested, so the same test performs differently in a herd with 10 percent prevalence than in one with 40 percent prevalence. Practitioners should calculate positive and negative predictive values for the specific herd before committing to a testing protocol.

Timing of Sampling and Its Influence on Interpretation

The postpartum day on which sampling occurs materially affects the interpretation of results. Blood BHB concentrations typically rise from day 2 to day 5 after calving and peak between days 5 and 7. Testing before day 3 may miss cows that have not yet developed ketonemia, while testing after day 7 may miss cows that have already recovered or progressed to clinical disease. A single sample provides a snapshot, not a trajectory, and cows with fluctuating ketone levels may be misclassified by any one-time measurement.

Calcium sampling is even more time-sensitive. Blood calcium nadirs within the first 24 to 48 hours after calving, and most cows that become hypocalcemic have recovered by day 3. Sampling later than day 2 will underestimate the true incidence of subclinical hypocalcemia. Serial sampling within the first three days provides a more complete picture but is logistically demanding on commercial dairies.

Integrating Multiple Data Streams

Modern dairy farms generate substantial data from milk recording, activity monitors, feed records, and health events. Combining these streams with targeted metabolic testing can improve the efficiency of transition cow monitoring. Milk production records, for example, can identify cows with poor early lactation performance that warrant blood testing. Genomic information, where available, adds a further layer of risk prediction, although its clinical utility for individual cow management remains under investigation. The integration of these data sources requires validation on each farm, because the relationships between predictor variables and disease outcomes vary with herd management, breed, and environment.

The practical challenge is not the availability of data but its interpretation. Predictive models developed in one population may not transfer to another, and the accuracy of any model depends on the quality and completeness of the underlying records. Practitioners should treat model outputs as screening tools that identify cows for confirmatory testing instead of as definitive diagnoses.

Diagnostic Decision Tree for Subclinical Transition Disease

Step 1: Risk Stratification Before Sampling

The diagnostic sequence begins before calving. Cows with parity greater than 2, previous lactation disease, body condition score above 3.5 at dry-off, or a history of dystocia or twins carry higher risk for subclinical ketosis, hypocalcemia, and metritis. Herds with monthly hyperketonemia prevalence above 15% warrant systematic monitoring instead of targeted testing of individual animals. USDA APHIS livestock disease information supports the use of national surveillance frameworks when designing herd-level monitoring programs.

For individual cow diagnostics, the decision to sample depends on the test's intended use. Milk Fourier-transform infrared spectroscopy models have demonstrated 83% sensitivity and 81% specificity for individual cow hyperketonemia detection, which is adequate for screening but not for confirmation. Big data and milk FTIR predictions for hyperketonemia management shows that prediction models built from test-day milk and performance variables lacked the sensitivity and specificity required for individual cow diagnostics, so confirmatory testing is mandatory when FTIR flags a cow.

Step 2: Blood Sampling Protocol and Point-of-Care Testing

Blood beta-hydroxybutyrate (BHB) remains the reference method for subclinical ketosis diagnosis. Sample between days 3 and 14 in milk, with day 5 to 7 as the optimal window for peak incidence. A handheld meter reading of 1.2 mmol/L or greater defines subclinical ketosis in most published protocols, while values above 3.0 mmol/L indicate clinical ketosis requiring immediate attention. The MSD Veterinary Manual provides the clinical context for interpreting these thresholds across production systems.

Sampling technique matters. Use a coccygeal vessel, collect into a heparinized tube, and test within 30 minutes. Whole blood, serum, and plasma yield different BHB values, so maintain consistency within a herd over time. Point-of-care meters calibrated for human use may under-read bovine blood, so validate each meter against a laboratory reference before adopting it for herd monitoring.

For subclinical hypocalcemia, blood total calcium below 2.0 mmol/L in the first 24 to 48 hours after calving is the commonly cited threshold, but ionized calcium measurement is more physiologically relevant. Ionized calcium below 1.0 mmol/L confirms subclinical disease. The challenge is that ionized calcium requires immediate analysis or anaerobic transport on ice, which limits its use in field settings. Total calcium corrected for albumin is a practical alternative when ionized measurement is unavailable.

Step 3: Uterine Disease Assessment

Subclinical metritis lacks a single definitive biomarker. The diagnosis rests on a combination of rectal palpation findings, vaginal discharge scoring, and systemic parameters. Perform transrectal palpation between days 4 and 7 postpartum. A uterus that fails to involute, feels doughy or thin-walled, and contains fetid fluid supports the diagnosis. Vaginal discharge scoring using a 0 to 3 scale, where 0 is clear lochia and 3 is fetid, red-brown, watery discharge, provides a reproducible assessment. A score of 2 or greater combined with rectal temperature above 39.5°C warrants classification as metritis, even when the cow appears bright.

The diagnostic challenge is that rectal temperature is an imperfect filter. Many cows with uterine infection remain normothermic, and fever can arise from mastitis, pneumonia, or other sources. The FAO animal production and health guidance emphasizes that production system context, including housing density and hygiene practices, should inform how aggressively uterine disease is pursued diagnostically.

Step 4: Interpretation Thresholds and Action Decisions

The thresholds above reflect commonly cited values in the veterinary literature, but regional and breed differences exist. WOAH terrestrial animal health standards note that diagnostic cut-offs should be validated for the population in which they are applied. A Jersey herd with a high prevalence of hypocalcemia may benefit from a lower action threshold than a Holstein herd, while pasture-based systems with seasonal calving may require different sampling intervals than confined systems with year-round calving.

Step 5: Documentation and Longitudinal Tracking

Record every test result in a format that allows trend analysis. For each cow, document parity, calving date, test date, test type, result, and any treatment administered. For each herd, calculate weekly prevalence of subclinical ketosis, hypocalcemia, and metritis. Prevalence above 15% for hyperketonemia indicates a nutritional management problem, not an individual cow problem, and should trigger diet evaluation instead of more testing.

The AVMA practice resources emphasize that diagnostic records serve both clinical and legal functions. Maintain records that would allow an independent veterinarian to reconstruct the diagnostic reasoning for any cow. Include the rationale for sampling, the test used, the result, and the decision made. This documentation supports both herd health audits and individual animal welfare assessments.

Step 6: Adjusting the Protocol to Farm Resources

The correct diagnostic approach depends on available equipment, labor, and laboratory access. A farm with a handheld BHB meter and no laboratory within reasonable transport distance should rely on point-of-care blood testing with periodic validation against a reference laboratory. A farm with milk recording and FTIR data can use that data for monthly herd prevalence monitoring, but must confirm individual cow positives with blood testing. Milk FTIR and genomic approaches to hyperketonemia demonstrates that FTIR-based models are suitable for herd-level prevalence estimation but require confirmatory testing for individual cow decisions.

Farms with limited labor should prioritize sampling at day 5 to 7 postpartum for all high-risk cows instead of sampling all cows daily. This targeted approach captures the peak incidence of subclinical ketosis while conserving resources. For metritis monitoring, daily visual assessment of vulvar discharge combined with weekly rectal palpation is a reasonable protocol for farms that cannot perform daily veterinary examinations.

When laboratory support is available, submit paired samples from a subset of cows to validate point-of-care devices. This is particularly important when changing meter brands, reagent lots, or sampling personnel. The cost of validation is small relative to the cost of acting on inaccurate results.

Recognized Failure Modes and Early Detection

Subclinical transition disease monitoring fails in characteriztic patterns. The most common is sampling too early. Cows sampled within the first 24 to 48 hours after calving frequently show blood beta-hydroxybutyrate (BHB) concentrations that have not yet peaked, and a cow that will develop hyperketonemia on day 5 may read normal on day 2. Conversely, sampling after day 14 misses the peak prevalence window for most metabolic disease. The second failure mode is reliance on a single test modality without understanding its operating characteriztics. Milk Fourier-transform infrared (FTIR) prediction models, for example, have demonstrated sufficient accuracy for monthly herd prevalence estimates but lack the sensitivity and specificity required for individual cow diagnostics, as reported in the symposium review on big data and hyperketonemia management from the Journal of Dairy Science Pralle and White, 2020. A veterinarian who treats individual cows based on an FTIR flag alone will both overtreat and miss cases.

A third failure mode is ignoring the cow's body condition and parity in interpretation. A thin, second-lactation cow with a BHB of 1.1 mmol/L has different clinical significance than an overconditioned third-lactation cow with the same value. The former may be in negative energy balance from inadequate dry matter intake, while the latter is mobilizing fat at a rate that predicts displaced abomasum and clinical ketosis. The fourth failure mode is poor sample handling. Blood samples left at room temperature for several hours show artefactual changes in glucose and enzyme activities, and milk ketone test strips stored in humid conditions degrade unpredictably.

Common Diagnostic Errors and Corrective Action

Less experienced clinicians tend to over-interpret a single blood BHB value. The threshold for subclinical ketosis, commonly cited as 1.2 mmol/L in blood, is a herd-level screening cut point, not a biological cliff. A cow at 1.1 mmol/L is not meaningfully different from one at 1.3 mmol/L, and the clinical response should be identical. The corrective action is to treat BHB as a continuous variable and pair it with history, milk production, and appetite.

A second error is neglecting the physical examination in favour of laboratory results. Subclinical disease is defined by the absence of clinical signs, but the transition cow examination should still include rumen fill score, rectal temperature, and uterine discharge assessment. A cow with a normal BHB but a foul-smelling vaginal discharge has subclinical metritis, and a cow with a displaced abomasum may have a normal BHB early in the disease course. Laboratory monitoring complements, but does not replace, the hands-on examination.

A third error is failing to account for the dynamic nature of the transition period. A single sampling event provides a snapshot. The diagnostic value of serial sampling, for example testing cows on days 3, 5, and 7 post-calving, is substantially higher than a single test, particularly for detecting cows that develop disease later in the first week. The corrective action is to build a sampling calendar that matches the farm's calving pattern and to review cumulative data monthly, not case by case.

Limitations of the Current Evidence

The evidence base for transition cow monitoring has genuine gaps. Most published thresholds for blood BHB and milk ketones derive from studies conducted in North American and European confinement systems, and their transferability to pasture-based systems, grazing herds, or tropical production environments is uncertain. The international guidance on livestock production systems from the FAO Animal Production and Health division emphasizes that monitoring protocols must be adapted to local feed resources and management systems, and the same principle applies to diagnostic thresholds.

Expert opinion still differs on the optimal sampling frequency and on whether routine testing of all cows is superior to targeted testing of at-risk groups. Some authorities advocate testing every cow between days 3 and 9 post-calving, while others argue that risk-based sampling, targeting overconditioned cows, twins, and cows with previous disease, is more cost-effective. The evidence does not clearly favour one approach, and the decision should be made at farm level based on labor availability and disease prevalence.

The predictive value of emerging biomarkers, including those being investigated for early disease detection in other species, is not yet established for transition cows. Work on chronic kidney disease in cats, for example, has shown that novel biomarkers can detect early metabolic changes before traditional measures become abnormal, as reviewed in the publication on feline chronic kidney disease progression Rosa et al., 2026. Whether analogous biomarkers will improve transition cow monitoring remains an open question, and clinicians should be cautious about adopting tests without local validation.

Referral, Consultation, and Regulatory Reporting

Most transition cow monitoring falls within routine herd health practice and does not require referral. Referral to a veterinary teaching hospital or a specialist in dairy production medicine is warranted when the farm experiences persistently high disease prevalence despite apparently correct monitoring and intervention, when there is suspicion of a nutritional formulation error that cannot be identified on farm, or when the veterinarian lacks the laboratory capacity to perform the required assays. Nutritionists and veterinary nutritionists are the appropriate consultants for ration reformulation, and diagnostic laboratories should be engaged when point-of-care results conflict with clinical observations.

Regulatory reporting obligations vary by jurisdiction. The WOAH terrestrial animal health standards define the international framework for notifiable disease reporting, and the USDA APHIS animal health information portal provides national program details for the United States. Transition cow metabolic disease is not notifiable in most jurisdictions, but the clinician should be alert to the possibility that an apparent metabolic problem masks an infectious disease with reporting requirements, such as bovine viral diarrhea virus or salmonellosis. When in doubt, the local veterinary authority should be contacted.

Frequently Asked Questions

How Should I Prioritize Testing When the Farm Has Limited Budget or Labor?

Start with the highest-yield, lowest-cost tests. Blood beta-hydroxybutyrate measurement using a point-of-care meter identifies subclinical ketosis with acceptable accuracy and requires minimal labor. If blood sampling is impractical, milk ketone test strips offer a reasonable alternative for cow-side screening. Reserve milk Fourier-transform infrared spectroscopy for herds where monthly test-day data are already collected, since the incremental cost is low. Prioritize cows in the highest-risk window, days 3 to 7 in milk, and focus on parity 2 and greater. When resources permit only one test per cow, use blood beta-hydroxybutyrate. For herds with no testing budget, monitor milk production and dry matter intake trends as indirect indicators, recognizing their limited sensitivity for individual cow diagnosis.

What Do I Do When Point-of-Care Ketone Meters Are Unavailable?

Laboratory serum or plasma beta-hydroxybutyrate measurement remains the reference method and can be used when cow-side devices are unavailable. Milk ketone tests, including semiquantitative strips and milk acetone tests, provide a practical substitute, although their sensitivity is lower than blood testing. Urine acetoacetate testing is inexpensive but has poor correlation with blood beta-hydroxybutyrate and produces more false positives, so interpret it cautiously. In herds with routine test-day milk recording, milk Fourier-transform infrared spectroscopy data can be obtained without additional farm labor, and prediction models have demonstrated adequate accuracy for monthly herd prevalence monitoring, though individual cow sensitivity and specificity remain limited. When no ketone testing is possible, rely on clinical examination and production records, and document the diagnostic limitation in the herd health record.

How Does Monitoring Differ in Beef Cattle or Small Ruminants During the Transition Period?

The transition period concept applies most directly to dairy cattle, where lactation demands create predictable metabolic stress. Beef cows experience lower milk production demands, so subclinical ketosis is less prevalent, and routine monitoring is rarely cost-effective. Targeted testing may be warranted in thin beef cows, cows with poor feed access, or those with concurrent disease. In sheep, pregnancy toxemia is the analogous condition, and it typically occurs in late gestation instead of early lactation. Blood beta-hydroxybutyrate measurement is diagnostically useful in ewes, but the timing, risk factors, and interpretation thresholds differ from dairy cows. For both beef cattle and small ruminants, consult species-specific reference ranges, since extrapolating dairy cow thresholds will misclassify animals.

What Records Should I Keep for Transition Cow Monitoring, and How Should I Use Them?

Record the cow identification, parity, calving date, test date, test method, and the numerical result for every animal sampled. Maintain a separate log of cows exceeding the action threshold, treatment administered, and subsequent disease events. Use these records to calculate weekly or monthly herd prevalence of subclinical ketosis, which should guide nutritional review. Longitudinal tracking allows you to detect changes in prevalence before clinical disease rates rise. Share the summary data with the nutritionist and farm manager at regular intervals. If you use milk Fourier-transform infrared spectroscopy predictions, store the raw test-day data and the prediction results so you can compare them against blood beta-hydroxybutyrate measurements taken in a validation subset.

How Should I Explain Subclinical Disease Findings to the Farm Owner or Manager?

Frame the conversation around production and financial outcomes instead of abstract physiology. Explain that subclinical ketosis does not produce visible signs but is associated with increased risk of displaced abomasum, clinical ketosis, and reduced milk yield. Present the herd prevalence figure and compare it with published benchmarks. Show the owner the distribution of test results, also the average, so they understand how many cows fall near the threshold. Describe the monitoring protocol as a recurring cost of doing business, similar to somatic cell count testing, instead of an emergency response. Emphasize that the goal is to detect problems early, when nutritional adjustments are more effective and less expensive than treating clinical disease.

When Should I Refer a Herd Problem to a Specialist or Diagnostic Laboratory?

Refer when herd prevalence of subclinical ketosis remains elevated despite nutritional intervention, when you suspect a feed quality or mycotoxin issue, or when the pattern of disease suggests an interaction between nutrition and infectious disease. A veterinary nutritionist can perform a full ration evaluation and dry matter intake assessment. A diagnostic laboratory can measure additional metabolites, including nonesterified fatty acids, to characterize the degree of negative energy balance more completely. If you observe unusual clinical signs, multiple disease syndromes, or suspected regulatory concerns, contact your regional veterinary diagnostic laboratory or the relevant animal health authority for guidance on sample submission and reporting requirements.

Related Clinical & Scientific Guides

• Rumen Health Assessment in Dairy Cows: Clinical and Subclinical Indicators
• Mastitis Control Programs in Dairy Herds: Monitoring and Prevention
• Swine Nutrition and Health: Feed-Related Disease Diagnosis

References and Further Reading

• Microfluidic innovations in chronic kidney disease and renal fibrosis: from mechanistic insights to clinical applications.. 2026.
• Understanding the Progression of Chronic Kidney Disease in Cats: From Pathophysiology to Emerging Biomarkers.. 2026.
• Analysis of matrix-bound nitrofuran residues in worldwide-originated honeys by isotope dilution high-performance liquid chromatography-tandem mass spectrometry.. 2004.
• Symposium review: Big data, big predictions: Utilizing milk Fourier-transform infrared and genomics to improve hyperketonemia management.. 2020.
• Determination of the antibiotic chloramphenicol in meat and seafood products by liquid chromatography-electrospray ionization tandem mass spectrometry.. 2003.
• Development of in-house ELISA for detection of chloramphenicol in bovine milk with subsequent confirmatory analysis by LC-MS/MS.. 2017.
• USDA APHIS Animal Health Information. USDA APHIS.
• FAO Animal Production and Health. FAO.
• MSD Veterinary Manual, Professional Edition. MSD Veterinary Manual.

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• Dairy Cow Nutrition: Diagnostic Approach to Metabolic Disorders

This article is educational professional reference material for veterinary audiences. It is not a substitute for veterinary diagnosis, individual clinical judgment, current product labeling, or applicable regulatory requirements.