Conditioned pain modulation in elite athletes: a systematic review and meta-analysis
Bibliographic record
Abstract
Background and aims Elite athletes reportedly have superior pain tolerances, but it is unclear if results extend to conditioned pain modulation (CPM). The aim of our study was to synthesize existing literature in order to determine whether CPM is increased in elite athletes compared to healthy controls. Methods A systematic review and random-effects meta-analysis was conducted. Cochrane Central Register of Controlled Trials, SPORTDiscus, PsycINFO, CINAHL, Web of Science, and PubMed were searched for English-language studies that examined CPM in adult elite athlete populations. Results Seven studies were identified; all were of poor to fair methodological quality. There was no overall difference in CPM between elite athletes and controls (Hedges g = 0.37, CI95 -0.03-0.76; p = 0.07). There was heterogeneity between studies, including one that reported significantly less CPM in elite athletes compared to controls. An exploratory meta-regression indicated that a greater number of hours trained per week was associated with higher CPM. Conclusions The overall number and quality of studies was low. Despite nominally favoring higher CPM in elite athletes, aggregate results indicate no significant difference compared to healthy controls. A possible factor explaining the high degree of variability between studies is the number of hours elite athletes spent training. Implications Based on available evidence, athletes do not have remarkable endogenous pain modulation compared to controls. High quality experimental studies are needed to address the effect of hours trained per week on CPM in athletes.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".