Multicenter preclinical studies as an innovative method to enhance translation: a systematic review of published studies
Bibliographic record
Abstract
Abstract Multicenter preclinical studies have been suggested as a method to improve reproducibility, generalizability and potential clinical translation of preclinical work. In these studies, multiple independent laboratories collaboratively conduct a research experiment using a shared protocol. The use of a multicenter design in preclinical experimentation is a recent approach and only a handful of preclinical multicenter studies have been published. Here, we systematically identify, assess and synthesize published preclinical multicenter studies investigating interventions using in vivo models. Synthesized data included study methods/design, basic characteristics, outcomes, and barriers and facilitators. Study risk of bias, completeness of reporting and the degree of collaboration were evaluated using established methods. The database searches identified 3095 citations and 12 studies met inclusion criteria. The multicenter study design was applied across a diverse range of diseases including stroke, heart attack, traumatic brain injury, and diabetes. The median number of centers was 4 (range 2-6) and the median sample size was 135 (range 23-384). Most studies had lower risk of bias and higher completeness of reporting than typically seen in single-centered studies. Only four of the twelve studies produced results consistent with previous single-center studies, highlighting a central concern of preclinical research: irreproducibility and poor generalizability of findings from single laboratories. Our review suggests that multicenter preclinical studies may provide a method to robustly assess therapies prior to considering clinical translation. Registered with PROSPERO CRD42018093986.
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 | 0.128 | 0.322 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.055 | 0.041 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".