Quality of Reporting of Clinical Trials of Dogs and Cats and Associations with Treatment Effects
Bibliographic record
Abstract
BACKGROUND: To address concerns about the quality of reporting of randomized controlled trials, and the potential for biased treatment effects in poorly reported trials, medical journals have adopted a common set of reporting guidelines, the Consolidated Standards of Reporting Trials (CONSORT) statement, to improve the reporting of randomized controlled trials. HYPOTHESIS: The reporting of clinical trials involving dogs and cats might not be ideal, and this might be associated with biased treatment effects. ANIMALS: Dogs and cats used in 100 randomly selected reports of clinical trials. METHODS: Data related to methodological quality and completeness of reporting were extracted from each trial. Associations between reporting of trial features and the proportion of positive treatment effects within trials were evaluated by generalized linear models. RESULTS: There were substantive deficiencies in reporting of key trial features. An increased proportion of positive treatment effects within a trial was associated with not reporting: the method used to generate the random allocation sequence (P < .001), the use of double blinding (P < .001), the inclusion criteria for study subjects (P = .003), baseline differences between treatment groups (P = .006), the measurement used for all outcomes (P = .002), and possible study limitations (P = .03). CONCLUSIONS AND CLINICAL IMPORTANCE: Many clinical trials involving dogs and cats in the literature do not report details related to methodological quality and aspects necessary to evaluate external validity. There is some evidence that these deficiencies are associated with treatment effects. There is a need to improve reporting of clinical trials, and guidelines, such as the CONSORT statement, can provide a valuable tool for meeting this need.
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 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 | 0.740 | 0.912 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".