The Devil Is in the Details: Incomplete Reporting in Preclinical Animal Research
Bibliographic record
Abstract
Incomplete reporting of study methods and results has become a focal point for failures in the reproducibility and translation of findings from preclinical research. Here we demonstrate that incomplete reporting of preclinical research is not limited to a few elements of research design, but rather is a broader problem that extends to the reporting of the methods and results. We evaluated 47 preclinical research studies from a systematic review of acute lung injury that use mesenchymal stem cells (MSCs) as a treatment. We operationalized the ARRIVE (Animal Research: Reporting of In Vivo Experiments) reporting guidelines for pre-clinical studies into 109 discrete reporting sub-items and extracted 5,123 data elements. Overall, studies reported less than half (47%) of all sub-items (median 51 items; range 37-64). Across all studies, the Methods Section reported less than half (45%) and the Results Section reported less than a third (29%). There was no association between journal impact factor and completeness of reporting, which suggests that incomplete reporting of preclinical research occurs across all journals regardless of their perceived prestige. Incomplete reporting of methods and results will impede attempts to replicate research findings and maximize the value of preclinical studies.
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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 | 0.756 | 0.882 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".