Reporting of IMMPACT-recommended core outcome domains among trials assessing opioids for chronic non-cancer pain
Bibliographic record
Abstract
The Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) has recommended that trialists evaluating treatments for chronic pain should consider reporting 9 patient-important outcome domains. We examined the extent to which clinical trials evaluating the effect of opioids for chronic non-cancer pain (CNCP) report outcome domains recommended by IMMPACT. We systematically searched electronic databases for English-language studies that randomized patients with CNCP to receive an opioid or a non-opioid control. In duplicate and independently, reviewers established the eligibility of each identified study and recorded all reported outcome domains from eligible trials. We conducted a priori regression analyses to explore factors that may be associated with IMMPACT-recommended outcome domains. Among 156 eligible trials, reporting of IMMPACT-recommended outcome domains was highly variable, ranging from 99% for pain to 7% for interpersonal functioning. Recently published trials were more likely to report the effect of treatment on physical functioning, emotional functioning, role functioning, sleep and fatigue, and participant disposition. Trials for which the corresponding author was from North America were more likely to report treatment effects on physical functioning and participant ratings of improvement and satisfaction with treatment. Trials published in higher impact journals were more likely to report treatment effects on emotional function, but less likely to report participant ratings of improvement and satisfaction with treatment. Most IMMPACT domains showed an increased rate of reporting over time, although many patient-important outcome domains remained unreported by over half of all trials evaluating the effects of opioids for CNCP.
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 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 | 0.625 | 0.823 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".