Measuring lameness prevalence: Effects of case definition and assessment frequency
Bibliographic record
Abstract
Lameness assessments are commonly conducted at a single point in time, but such assessments are subject to multiple sources of error. We conducted a longitudinal study, assessing the gait of 282 lactating dairy cows weekly during the first 12 wk of lactation, with the aim of assessing how lameness prevalence changed in relation to case definition and assessment frequency. Gait was scored using a 5-point scale where scores of 1 and 2 were considered sound, 3 was clinically lame, and 4 and 5 were severely lame. We created 5 lameness definitions using increasingly stringent thresholds based upon the number of consecutive events of locomotion score ≥3. In LAME1, a cow was considered lame when locomotion score was ≥3 at any scoring event, in LAME2, LAME3, LAME4, and LAME5, a cow was considered lame when locomotion score was 3 or higher during 2, 3, 4, and 5 consecutive scoring events, respectively. We also assessed the effect of assessment frequency on measures of prevalence and incidence using weekly assessment (ASSM1), 1 assessment every 2 wk (ASSM2), 1 assessment every 3 wk (ASSM3), and 1 assessment every 4 wk (ASSM4). Using LAME1, 69.2% of cows were considered lame at some point during the trial, with an average point prevalence of 31.8% (SD: 2.8) and average incidence rate of 10.9 cases/100 cow weeks (SD: 3.7). Lameness prevalence decreased to 28.0% when using LAME5. Survival analysis was used to assess the effects of parity, using these different case definitions. Parity is a known risk for lameness, such that case definitions and prevalence estimates should be stratified by parity to inform management decisions. Using the LAME3 criterion, primiparous cows had the highest chance of reaching 12 wk without a lameness event, and fourth and higher parities had the lowest. Weighted linear and quadratic kappa values were used to assess agreement between different assessment frequencies and lameness definitions; we found substantial to excellent agreement between ASSM1 and ASSM2 using LAME1, LAME2, and LAME3 definitions. Agreement was fair to substantial between ASSM1 and ASSM3 and low to fair between ASSM1 and ASSM4. Likewise, the agreement between LAME1 and LAME2 was fair in primiparous cows, substantial in second and third parity cows, and poor to fair in fourth and greater parity cows. We conclude that lameness prevalence estimates are dependent upon case definition and that the use of more stringent case definitions results in fewer cows classified as lame. These results suggest that routine locomotion assessments be conducted at least every 2 wk, and that cows should be defined as lame on the basis of 2 consecutive assessments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".