Current Status and Future Directions of Pain-Related Outcome Measures for Post-Surgical Pain Trials
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it