Measuring lameness prevalence: Effects of case definition and assessment frequency
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,095 | 0,238 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».