Incomplete reporting of experimental studies and items associated with risk of bias in veterinary research
Bibliographic record
Abstract
In in vivo research, the reporting of core items of study design is persistently poor, limiting assessment of study quality and study reproducibility. This observational cohort study evaluated reporting levels in the veterinary literature across a range of species, journals and research fields. Four items (randomisation, sample size estimation, blinding and data exclusion) were assessed as well as availability of study data in publicly accessible repositories. From five general and five subject-specific journals, 120 consecutively published papers (12 per journal) describing in vivo experimental studies were selected. Item reporting was scored using a published scale (items ranked as fully, partially or not reported) according to completeness of reporting. Papers in subject-specific journals had higher median reporting levels (50.0 per cent vs 33.3 per cent, P=0.007). In subject-specific journals, randomisation (75.0 per cent vs 41.7 per cent, P=0.0002) and sample size estimation (35.0 per cent vs 16.7 per cent, P=0.025) reporting was approximately double that of general journals. Blinding (general 48.3 per cent, subject-specific 50.0 per cent, P=0.86) and data exclusion (general 53.3 per cent, subject-specific 63.3 per cent, P=0.27) were similarly reported. A single paper made study data readily accessible. Incomplete reporting remains prevalent in the veterinary literature irrespective of journal type, research subject or species. This impedes evaluation of study quality and reproducibility, raising concerns regarding wasted financial and animal resources.
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 | 0.629 | 0.775 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".