Systematic review of movement-evoked pain versus pain at rest in postsurgical clinical trials and meta-analyses: A fundamental distinction requiring standardized measurement
Bibliographic record
Abstract
To estimate frequency of movement-evoked pain (MEP) measurement in human postsurgical investigations, we reviewed thoracotomy, knee arthroplasty, and hysterectomy clinical trials and meta-analyses. Only 39% of trials measured MEP and 52% failed to identify pain outcome as pain at rest (PAR) or MEP. Temporal trending did not suggest that MEP measurement is becoming more frequent. Trials measuring both MEP and PAR suggest that MEP is 95-226% more intense than PAR in the first 3 postoperative days. Among trials measuring MEP, 38% did not specify the physical maneuver used to assess MEP. Five of 7 meta-analyses reviewed (71%) did not distinguish between PAR and MEP, and none of the 7 meta-analyses declared the 20-59% of reviewed trials that had failed to identify their pain outcome as PAR or MEP. These results suggest an unchanging neglect of MEP in postsurgical pain trials and frequent failure to identify pain outcome as PAR or MEP. This is an important problem because MEP is usually more severe than PAR; MEP exerts a more direct adverse impact on postsurgical functional recovery and several current and novel pain treatments differentially affect MEP vs PAR. Failure to distinguish between PAR and MEP and standardize their measurement threatens trial precision and ability to identify interventions with the most clinically relevant effects on pain. We therefore recommend developing consistent terminology regarding PAR and MEP, considering inclusion of MEP as a pain outcome in every postsurgical trial, and standardizing measurement of PAR and MEP on a procedure-specific basis.
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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 | 0.064 | 0.147 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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; 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".