Research designs for proof-of-concept chronic pain clinical trials: IMMPACT recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proof-of-concept (POC) clinical trials play an important role in developing novel treatments and determining whether existing treatments may be efficacious in broader populations of patients. The goal of most POC trials is to determine whether a treatment is likely to be efficacious for a given indication and thus whether it is worth investing the financial resources and participant exposure necessary for a confirmatory trial of that intervention. A challenge in designing POC trials is obtaining sufficient information to make this important go/no-go decision in a cost-effective manner. An IMMPACT consensus meeting was convened to discuss design considerations for POC trials in analgesia, with a focus on maximizing power with limited resources and participants. We present general design aspects to consider including patient population, active comparators and placebos, study power, pharmacokinetic-pharmacodynamic relationships, and minimization of missing data. Efficiency of single-dose studies for treatments with rapid onset is discussed. The trade-off between parallel-group and crossover designs with respect to overall sample sizes, trial duration, and applicability is summarized. The advantages and disadvantages of more recent trial designs, including N-of-1 designs, enriched designs, adaptive designs, and sequential parallel comparison designs, are summarized, and recommendations for consideration are provided. More attention to identifying efficient yet powerful designs for POC clinical trials of chronic pain treatments may increase the percentage of truly efficacious pain treatments that are advanced to confirmatory trials while decreasing the percentage of ineffective treatments that continue to be evaluated rather than abandoned.
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.
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 | 0.773 | 0.965 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it