Evaluation of infrared thermography as a non-invasive tool for detection of subclinical laminitis in dairy cattle
Bibliographic record
Abstract
Lameness is the second most costly production disease in dairy cattle after mastitis, yet subclinical laminitis—defined as claw horn lesions detectable only at trimming—often goes unrecognised until it progresses to clinical lameness with visible gait abnormality. While locomotion scoring identifies overtly lame cows, it cannot detect the early inflammatory changes within the corium that precede visible sole haemorrhage or white line disease. Infrared thermography (IRT) offers a contactless, rapid means of measuring surface temperature differences that may reflect subclinical corium inflammation. This research evaluated the diagnostic performance of IRT for detecting subclinical laminitis in 155 Holstein dairy cows at a commercial free-stall facility near Saskatoon, Saskatchewan, Canada. Coronary band and dorsal sole surface temperatures were recorded using a handheld thermal camera (FLIR E60, resolution 320 × 240 pixels) immediately before routine hoof trimming. Temperature difference from ambient (?T) was calculated and compared against the trimming-based lesion score (0-4 scale) as the gold standard. A total of 126 of 155 cows (81.3%) had at least one claw lesion at trimming, with sole haemorrhage (28.4%) and white line disease (18.7%) the most prevalent. Coronary band ?T was positively correlated with lesion score (r = 0.71, P<0.001). Receiver operating characteristic analysis identified an optimal ?T threshold of 2.5°C for distinguishing cows with lesion score ?2 from those with score <2, yielding a sensitivity of 82.4% and specificity of 74.1% (AUC = 0.84). These findings suggest that IRT screening at a ?T threshold of 2.5°C can identify the majority of subclinically laminitic cows and may serve as a practical on-farm triage tool to prioritise animals for early therapeutic trimming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".