Quality of reporting of clinical trials in dogs and cats: An update
Bibliographic record
Abstract
BACKGROUND: Comprehensive reporting of clinical trials is essential to allow the trial reader to evaluate the methodological rigor of the trial and interpret the results. Since publication of the updated Consolidated Standards of Reporting Trials (CONSORT) guidelines for reporting of parallel clinical trials in humans, extensions for reporting of abstracts and crossover trials have been published. OBJECTIVES: To describe the types of trials using dogs and cats published from 2015 to 2020 and to evaluate the quality of reporting of a sample of recently published parallel and crossover trials. ANIMALS: None. METHODS: A comprehensive search was conducted to identify parallel or crossover design clinical trials using dogs and cats published from January 1, 2015 onwards. Quality of reporting was evaluated on a subset of trials published during 2019. The reporting of items recommended in the CONSORT reporting guidelines for abstracts, parallel trials, and crossover trials was evaluated independently by 2 reviewers using standardized forms created for this study. Disagreements among reviewers were resolved by consensus. Results were tabulated descriptively. RESULTS: The frequency of reporting of trial features varied from low to high. There remain deficiencies in the quality of reporting of key methodological features and information needed to evaluate and interpret trial results. CONCLUSIONS AND CLINICAL IMPORTANCE: There is still a need for authors, peer-reviewers, and editors to follow reporting guidelines such as CONSORT to maximize the value of clinical trials and to increase confidence in the validity of the trial results.
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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 | 0.629 | 0.795 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.034 | 0.040 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".