Systematic review of reviews including animal studies addressing therapeutic interventions for sepsis*
Bibliographic record
Abstract
OBJECTIVE: Certain methodologic features of animal experiments such as random assignment have been found to reduce the risk of bias. Because animal research sometimes informs clinical practice, explicit acknowledgment of the risk of bias and clinical relevance cultivates realistic expectations on the part of clinicians reading preclinical studies. We assessed literature reviews of therapeutic interventions for sepsis that include animal experiments for explicit appraisals of the risk of bias and clinical relevance. DATA SOURCES: MEDLINE and EMBASE. STUDY SELECTION: Systematic reviews or meta-analyses of animal experiments focusing on therapeutic interventions for sepsis. DATA EXTRACTION: In teams of two, reviewers independently screened citations and abstracted data. We determined whether the reviews systematically incorporated critical appraisals for the risk of bias and clinical relevance of the underlying studies as well as explicit extrapolations from preclinical research to human patients. DATA SYNTHESIS: From 164 citations, we retained 45 reviews. Chance-corrected agreement for inclusion was moderate (κ 0.57). Three (7%) met our criteria for a systematic review and one (2%) systematically appraised the risk of bias and the clinical relevance of the primary animal experiments. Thirty-six (80%) were narrative reviews addressing issues related to diverse topics such as pathophysiology and diagnosis as well as multiple therapies and 40 of 45 (89%) included both clinical and animal studies. Twelve (27%) explicitly assumed that data from preclinical studies could apply to human patients. CONCLUSIONS: Although a significant proportion of reviews extrapolated preclinical study results to human patients, most did not systematically appraise the risk of bias or the clinical relevance of preclinical research. Because animal experiments may influence clinical practice, we propose a framework to enhance these features in future reviews of preclinical research.
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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 | 0.055 | 0.237 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.012 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".