Quality of reporting of otorhinolaryngology articles using animal models with the ARRIVE statement
Bibliographic record
Abstract
Research involving animal models is crucial for the advancement of science, provided that experiments are designed, performed, interpreted, and reported well. In order to investigate the quality of reporting of articles in otorhinolaryngology research using animal models, a PubMed database search was conducted to retrieve eligible articles. The checklist of the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines was used to assess the quality of reporting of articles published in ear, nose and throat (ENT) and multidisciplinary journals. Two authors screened titles, abstracts, and full texts to select articles reporting otorhinolaryngology research using in vivo animal models. ENT journals ( n = 35) reported a mean of 57.1% adequately scored ARRIVE items (median: 58.3%; 95% confidence interval [CI; 53.4-60.9%]), while articles published in multidisciplinary journals ( n = 36) reported a mean of 49.1% adequately scored items (median: 50.0; 95% CI [46.2-52.0%]). Articles published in ENT journals showed better quality of reporting of animal studies based on the ARRIVE guidelines ( P < 0.05). However, adherence to the ARRIVE guidelines is generally poor in otorhinolaryngology research using in vivo animal models. The endorsement of the ARRIVE guidelines by authors, research and academic institutes, editorial offices and funding agencies is recommended for improved reporting of scientific research using animal models.
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 | 0.390 | 0.652 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.040 | 0.025 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".