215 Assessment of the Diagnostic Sensitivity and Specificity of Pain Biomarkers in Cattle Using Receiver Operating Characteristic Curves
Bibliographic record
Abstract
Abstract Biomarkers are commonly used to assess pain and analgesic drug efficacy in livestock. However, the diagnostic sensitivity and specificity of these biomarkers for different pain conditions over time have not been described. Receiver operating characteristic (ROC) curves are graphical plots that illustrate the diagnostic ability of a test as its discrimination threshold is varied. The objective of this analysis was to use area under the curve (AUC) values derived from ROC analysis to assess the predictive value of pain biomarkers at specific timepoints. The biomarkers included in the analysis were blood cortisol, salivary cortisol, hair cortisol, infrared thermography (IRT), mechanical nociceptive threshold (MNT), substance P, and outcomes from a pressure/force measurement system and visual analog scale. A total sample size of 7,992 biomarker outcomes were collected from 6 pain studies involving pain associated with castration, dehorning, lameness, and surgery were included in the analysis. Each study consisted of three treatments; pain, no pain, and analgesia. All statistics were performed using statistical software (JMP Pro 14.0, SAS Institute, Inc., Cary, NC). Results comparing analgesia verses pain yielded good diagnostic accuracy (AUC > 0.7; 95% CI: 0.40 to 0.99) for blood cortisol (timepoints 1.5, 2, and 6 hours); IRT (timepoints 6, 8, 12, and 72 hours); and MNT (timepoints 6, 25, and 49 hours). These results indicate that ROC analysis can be a useful indicator of the predictive value of pain biomarkers and certain timepoints seem to yield good diagnostic accuracy while many do not.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".