A systematic review and meta-analysis of pregabalin preclinical studies
Bibliographic record
Abstract
Despite large efforts to test analgesics in animal models, only a handful of new pain drugs have shown efficacy in patients. Here, we report a systematic review and meta-analysis of preclinical studies of the commercially successful drug pregabalin. Our primary objective was to describe design characteristics and outcomes of studies testing the efficacy of pregabalin in behavioral models of pain. Secondarily, we examined the relationship between design characteristics and effect sizes. We queried MEDLINE, Embase, and BIOSIS to identify all animal studies testing the efficacy of pregabalin published before January 2018 and recorded experimental design elements addressing threats to validity and all necessary data for calculating effect sizes, expressed as the percentage of maximum possible effect. We identified 204 studies (531 experiments) assessing the efficacy of pregabalin in behavioral models of pain. The analgesic effect of pregabalin was consistently robust across every etiology/measure tested, even for pain conditions that have not responded to pregabalin in patients. Experiments did not generally report using design elements aimed at reducing threats to validity, and analgesic activity was typically tested in a small number of model systems. However, we were unable to show any clear relationships between preclinical design characteristics and effect sizes. Our findings suggest opportunities for improving the design and reporting of preclinical studies in pain. They also suggest that factors other than those explored in this study may be more important for explaining the discordance between outcomes in animal models of pain and those in clinical trials.
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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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".