The standards of reporting trials in pets (PetSORT): Explanation and elaboration
Bibliographic record
Abstract
Well-designed randomized controlled trials (RCTs) provide the best evidence of the primary research designs for evaluating the effectiveness of interventions. However, if RCTs are incompletely reported, the methodological rigor with which they were conducted cannot be reliably evaluated and it may not be possible to replicate the intervention. Missing information also may limit the reader's ability to evaluate the external validity of a trial. Reporting guidelines are available for clinical trials in human healthcare (CONSORT), livestock populations (REFLECT), and preclinical experimental research involving animals (ARRIVE 2.0). The PetSORT guidelines complement these existing guidelines, providing recommendations for reporting controlled trials in pet dogs and cats. The rationale and scientific background are explained for each of the 25 items in the PetSORT reporting recommendations checklist, with examples from well-reported trials.
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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 | 0.472 | 0.797 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.034 | 0.022 |
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".