Core outcome domains for chronic pain clinical trials: IMMPACT recommendations
Bibliographic record
Abstract
OBJECTIVE: To provide recommendations for the core outcome domains that should be considered by investigators conducting clinical trials of the efficacy and effectiveness of treatments for chronic pain. Development of a core set of outcome domains would facilitate comparison and pooling of data, encourage more complete reporting of outcomes, simplify the preparation and review of research proposals and manuscripts, and allow clinicians to make informed decisions regarding the risks and benefits of treatment. METHODS: Under the auspices of the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT), 27 specialists from academia, governmental agencies, and the pharmaceutical industry participated in a consensus meeting and identified core outcome domains that should be considered in clinical trials of treatments for chronic pain. CONCLUSIONS: There was a consensus that chronic pain clinical trials should assess outcomes representing six core domains: (1) pain, (2) physical functioning, (3) emotional functioning, (4) participant ratings of improvement and satisfaction with treatment, (5) symptoms and adverse events, (6) participant disposition (e.g. adherence to the treatment regimen and reasons for premature withdrawal from the trial). Although consideration should be given to the assessment of each of these domains, there may be exceptions to the general recommendation to include all of these domains in chronic pain trials. When this occurs, the rationale for not including domains should be provided. It is not the intention of these recommendations that assessment of the core domains should be considered a requirement for approval of product applications by regulatory agencies or that a treatment must demonstrate statistically significant effects for all of the relevant core domains to establish evidence of its efficacy.
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 | 0.463 | 0.601 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.020 | 0.015 |
| Research integrity | 0.061 | 0.054 |
| Insufficient payload (model declined to judge) | 0.012 | 0.018 |
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".