Evaluating the Association Between Acute and Chronic Pain After Surgery
Bibliographic record
Abstract
AIM/OBJECTIVES/BACKGROUND: There is a need to predict chronic (Z3mo) postsurgical pain (CPSP). Acute (<7 d) pain is a predictor, that is, more severe pain is associated with higher CPSP risk. However, reported associations vary widely. METHODS: Using a systematic search, we examined associations between 2 acute pain measures (pain at rest [PAR] and movement-evoked pain [MEP]) and CPSP outcomes (considering severity vs. any "nonzero" pain only) in 22 studies. RESULTS: Seven studies reported the relationship between CPSP and both PAR and MEP. Of these, 2/7 reported no association, 3/7 reported significant associations for both PAR and MEP, 1/7 reported an association for PAR only, and 1/7 reported an association for MEP only. Six of another 7 studies reporting only the association for MEP found a significant relationship. Three of the 5 studies that did not specify whether acute pain outcomes were PAR or MEP reported a significant relationship. Another 3 studies reporting a relationship with CPSP did not specify whether this was for PAR, MEP, or both. All investigations incorporating severity of CPSP in their analyses (n=7) demonstrated a significant relationship, whereas only 10 of the 15 studies that dichotomized CPSP outcome as "no pain" versus "any"/"nonzero pain" were positive. CONCLUSIONS: Overall, evidence for an association between acute and chronic pain is moderate at best. However, closer attention to pain measurement methods will clarify the relationships between acute pain and CPSP. We propose that future CPSP predictor studies assess both PAR and MEP acutely and also incorporate CPSP severity in their analyses.
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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 | 0.025 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".