Inter-observer Agreement on a Checklist to Evaluate Scientific Publications in the Field of Animal Reproduction
Bibliographic record
Abstract
This study's objective was to determine respondents' inter-observer agreement on a detailed checklist to evaluate three exemplars (one case report, one randomized controlled study without blinding, and one blinded, randomized controlled study) of the scientific literature in the field of bovine reproduction. Fourteen international scientists in the field of animal reproduction were provided with the three articles, three copies of the checklist, and a supplementary explanation. Overall, 13 responded to more than 90% of the items. Overall repeatability between respondents using Fleiss's κ was 0.35 (fair agreement). Combining the "strongly agree" and "agree" responses and the "strongly disagree" and "disagree" responses increased κ to 0.49 (moderate agreement). Evaluation of information given in the three articles on housing of the animals (35% identical answers) and preconditions or pretreatments (42%) varied widely. Even though the overall repeatability was fair, repeatability concerning the important categories was high (e.g., level of agreement=98%). Our data show that the checklist is a reasonable and practical supporting tool to assess the quality of publications. Therefore, it may be used in teaching and practicing evidence-based veterinary medicine. It can support training in systematic and critical appraisal of information and in clinical decision making.
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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 | 0.250 | 0.346 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".