Identifying and understanding factors that affect the translation of therapies from the laboratory to patients: a study protocol
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> The process of translating preclinical findings into a clinical setting takes decades. Previous studies have suggested that only 5-10% of the most promising preclinical studies are successfully translated into viable clinical applications. The underlying determinants of this low success rate (e.g. poor experimental design, suboptimal animal models, poor reporting) have not been examined in an empirical manner. Our study aims to determine the contemporary success rate of preclinical-to-clinical translation, and subsequently determine if an association between preclinical study design and translational success/failure exists. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> Established systematic review methodology will be used with regards to the literature search, article screening and study selection process. Preclinical, basic science studies published in high impact basic science journals between 1995 and 2015 will be included. Included studies will focus on publicly available interventions with potential clinical promise. The primary outcome will be successful clinical translation of promising therapies - defined as the conduct of at least one Phase II trial (or greater) with a positive finding. A case-control study will then be performed to evaluate the association between elements of preclinical study design and reporting and the likelihood of successful translation. </ns4:p> <ns4:p> <ns4:bold>Discussion:</ns4:bold> This study will provide a comprehensive analysis of the therapeutic translation from the laboratory bench to the bedside. Importantly, any association between factors of study design and the success of translation will be identified. These findings may inform future research teams attempting preclinical-to-clinical translation. Results will be disseminated to identified knowledge users that fund/support preclinical research. </ns4:p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".