Predictors of response to full agonist opioids in enriched enrollment randomized withdrawal clinical trials: a participant-level data meta-analysis
Bibliographic record
Abstract
ABSTRACT: This study aims to identify predictors of success in treating chronic pain patients with full agonist opioids by analyzing harmonized individual patient data from 5594 participants in 9 enriched enrollment randomized withdrawal clinical trials available in the Food and Drug Administration data repository. We analyzed both the participants' success with titration and continued success in the 84-day maintenance phases after randomization for those maintained on the drug. We used the full data set to assess participant demographics and subsets of data containing participant reported outcomes at baseline. Participants had an average age of 51, with 55% female participants and 66% non-Hispanic white. No clinically relevant differences were observed between participants who failed titration or those who continued on full agonists through the maintenance phase. Prediction models were developed using mixed effects logistic regression and generalized linear mixed models, with the study as a random effect to account for inter-study differences. Despite large numbers, the analysis did not reveal clinically useful prediction models for either the titration or maintenance phase; however, higher initial pain scores were modest predictors of poorer outcomes. No patient-reported outcome measures were predictive of responses to therapy. The study's limitations include its volunteer-based sample and the exclusion criteria, although excluding patients with opioid use disorder or serious psychological conditions are similar to those used in clinical care. As no strong predictive factors for successful treatment were identified, the decision to use opioids to treat chronic pain requires careful clinical judgment and close monitoring.
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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 | 0.049 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.033 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".