Algometer Precision for Quantifying Mechanical Nociceptive Threshold When Applied to the Udder of Lactating Dairy Cows
Bibliographic record
Abstract
Objectives of this study were to: (1) quantify the reliability of an algometer for measuring mechanical nociceptive thresholds when applied to the udder of dairy cows; and (2) evaluate whether covariates, such as cow characteristics or time of the day, would influence algometer measurements. This prospective study was performed in a university herd of 37 lactating cows during five consecutive days, involving two raters. Two types of measurement were obtained: one qualitative binary measure (i.e., reaction vs. no reaction) and one quantitative measure presented in kilograms (i.e., mechanical nociceptive threshold, MNT) for the cows that reacted. Kappa statistics were used to investigate test-retest and inter-rater reliability for the qualitative measure, while concordance correlation coefficient (CCC) and limits of agreement plot were used for the quantitative measure. Whether algometer measurements were influenced by several covariates (i.e., time of the day, level of milk production, days in milk, and parity) was then evaluated using logistic or linear regression models, depending on the outcome. The algometer was moderately reliable; there was moderate test-retest reliability (Kappa = 0.53; CCC = 0.58) and inter-rater reliability (Kappa = 0.42; CCC = 0.54). The MNT varied substantially as a function of time of the day and parity. This is the first study reporting reliability of a pressure algometer for quantifying MNT and investigating covariates possibly affecting this measurement when applied to the udder of dairy cows. It is concluded that the use of an algometer for quantifying MNT on the udder is only moderately repeatable and is influenced by extraneous covariates. Its usage in research setting to quantify changes in sensitivity at the udder level should, therefore, be considered very cautiously or it should be further developed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".