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Record W2115281457 · doi:10.1136/vr.101934

Keel fracture assessment of laying hens by palpation: inter‐observer reliability and accuracy

2013· article· en· W2115281457 on OpenAlexaff
Mike Petrik, Michele T. Guerin, Tina M. Widowski

Bibliographic record

VenueVeterinary Record · 2013
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsKeelPalpationFlockMedicineReliability (semiconductor)RepeatabilityVeterinary medicinePhysical therapyOrthodonticsSurgeryMathematicsStatistics

Abstract

fetched live from OpenAlex

The objective of this study was to assess the interobserver reliability (agreement) and accuracy of keel palpation for the purpose of detecting old fractures in an end-of-lay flock of commercial laying hens. The low level of invasiveness and the relative speed at which this evaluation can be carried out lends itself well to use in a welfare audit, but only if the results are reliable and accurate from various assessors. The palpation technique first described by Wilkins and others (2004) was used to manually palpate for keel fractures. The technique was modified in that only keel fractures were considered. Eight assessors with varying laying hen experience palpated 100 live ISA Brown hens that had been in lay for 49 weeks. The hens were then euthanased and examined by dissection to establish whether there had been a keel fracture present (yes/no). The accuracy for individual assessors ranged from 87.1 to 96.8 per cent, with a mean of 91.8 per cent among all eight assessors. The interobserver reliability among all eight assessors was moderate (κ=0.44). Accuracy and κ values were 84.8 per cent and 0.41 for the first 50 hens, and 99.5 per cent and 0.47 for the last 50 hens, respectively, indicating that there was increased accuracy and agreement as the assessors became more experienced at palpation. This level of agreement, and the high level of accuracy, would make this technique an acceptable measure of keel fracture prevalence in a welfare audit.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.063
GPT teacher head0.362
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2013
Admission routes1
Has abstractyes

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