Pain Perception in Contact Sport Athletes: A Scoping Review
Bibliographic record
Abstract
Contact sports athletes are regularly facing acute physical pain in part of their practice. However, the literature investigating pain perception in these athletes remains scarce. This scoping review aimed to explore the literature surrounding pain perception in contact sport athletes and to compile and understand how it is studied. The search strategy consisted of using index terms and keywords in Medline, EMBASE, Sport-Discus, Web of Science, PsycINFO, CINAHL and ProQuest Dissertations & Theses Global search engines. Results revealed that a mix of team contact sports and combat sports are studied and included under the umbrella of contact sports. These athletes are being compared to non-athletes as well as athletes from non-contact sports. The cold pressor test and the pain pressure test are the two predominant methods used to investigate physical pain. This review highlights the need to clearly define sports based on contact levels expected in play to better define the types of pain athletes are facing in their practice. Athlete’s level of play as well as years of experience should also be more rigorously reported. While contact sport athletes seem to have a higher level of pain tolerance than both active controls and non-contact athletes, the methods of pain testing are not always justified and appropriate in relation to the pain induced during contact sports. Future experimental studies should use pain testing methods relevant to the pain experienced during contact sports and better justify the rationale for the choice of these methods.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".