Systematic review of enriched enrolment, randomised withdrawal trial designs in chronic pain
Bibliographic record
Abstract
Enriched enrolment, randomised withdrawal (EERW) pain trials select, before randomisation, patients who respond by demonstrating a predetermined degree of pain relief and acceptance of adverse events. There is uncertainty over the value of this design. We report a systematic review of EERW trials in chronic noncancer pain together with a critical appraisal of methods and potential biases in the methods used and recommendations for the design and reporting of future EERW trials. Electronic and other searches found 25 EERW trials published between 1995 and June 2014, involving 5669 patients in a randomised withdrawal phase comparing drug with placebo; 13 (median, 107 patients) had a randomised withdrawal phase of 6 weeks or less, and 12 (median, 334) lasted 12 to 26 weeks. Risks of bias included short duration, inadequate outcome definition, incomplete outcome data reporting, small size, and inadequate dose tapering on randomisation to placebo. Active treatment was usually better than placebo (22/25 trials). This review reduces the uncertainty around the value of EERW trials in pain. If properly designed, conducted, and reported, they are feasible and useful for making decisions about pain therapies. Shorter, small studies can be explanatory; longer, larger studies can inform practice. Current evidence is inadequate for valid comparisons in outcome between EERW and classical trials, although no gross differences were found. This systematic review provides a framework for assessing potential biases and the value of the EERW trials, and for the design of future studies by making recommendations for the conduct and reporting of EERW 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 | 0.118 | 0.372 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| 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".