Current Status and Future Directions of Pain-Related Outcome Measures for Post-Surgical Pain Trials
Bibliographic record
Abstract
Background: Clinical trials remain vital in order to: A) develop new treatment interventions, and also, B) to guide optimal use of current interventions for the treatment and prevention of acute and chronic postsurgical pain. Measures of pain (e.g. intensity and relief) and opioid use have been validated for the settings of postsurgical pain and continue to effectively guide research in this field..Methods: This narrative review considers needs for innovation in postsurgical pain trial outcomes assessment.Results: Future improvements are needed and include: A) more widespread measurement of movement-evoked pain with validation of various procedure-relevant movemen-tevoked pain maneuvers; B) new validated analytical approaches to integrate early postoperative pain scores with opioid use; and, C) closer attention to the measurement of postoperative opioid use after hospital discharge. In addition to these traditional measures, consideration is being given to the use of new pain-relevant outcome domains that include: 1) other symptoms (e.g. nausea and vomiting), 2) recovery of physiological function (e.g. respiratory, gastrointestinal, genitourinary and musculoskeletal), 3) emotional function (e.g. depression, anxiety) and, 4) development of chronic postsurgical pain. Also, there is a need to develop pain-related domains and measures for evaluating both acute and chronic post-operative pain. Finally, evidence suggests that further needs for improvements in safety assessment and reporting in postsurgical pain trials is needed, e.g. by using an agreed upon, standardized collection of outcomes that will be reported as a minimum in all postsurgical pain trials.Conclusions: These proposed advances in outcome measurement methodology are expected to improve the success by which postsurgical pain trials guide improvements in clinical care and patient outcomes.
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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 | 0.486 | 0.492 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.015 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".