Epidemiology and reporting characteristics of preclinical systematic reviews
Bibliographic record
Abstract
In an effort to better utilize published evidence obtained from animal experiments, systematic reviews of preclinical studies are increasingly more common-along with the methods and tools to appraise them (e.g., SYstematic Review Center for Laboratory animal Experimentation [SYRCLE's] risk of bias tool). We performed a cross-sectional study of a sample of recent preclinical systematic reviews (2015-2018) and examined a range of epidemiological characteristics and used a 46-item checklist to assess reporting details. We identified 442 reviews published across 43 countries in 23 different disease domains that used 26 animal species. Reporting of key details to ensure transparency and reproducibility was inconsistent across reviews and within article sections. Items were most completely reported in the title, introduction, and results sections of the reviews, while least reported in the methods and discussion sections. Less than half of reviews reported that a risk of bias assessment for internal and external validity was undertaken, and none reported methods for evaluating construct validity. Our results demonstrate that a considerable number of preclinical systematic reviews investigating diverse topics have been conducted; however, their quality of reporting is inconsistent. Our study provides the justification and evidence to inform the development of guidelines for conducting and reporting preclinical systematic reviews.
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 | 0.302 | 0.779 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.041 | 0.055 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".