A systematic literature review of 10 years of research on sex/gender and experimental pain perception – Part 1: Are there really differences between women and men?
Bibliographic record
Abstract
The purpose of this systematic review was to summarize and critically appraise the results of 10 years of human laboratory research on pain and sex/gender. An electronic search strategy was designed by a medical librarian and conducted in multiple databases. A total of 172 articles published between 1998 and 2008 were retrieved, analyzed, and synthesized. The first set of results (122 articles), which is presented in this paper, examined sex difference in the perception of laboratory-induced thermal, pressure, ischemic, muscle, electrical, chemical, and visceral pain in healthy subjects. This review suggests that females (F) and males (M) have comparable thresholds for cold and ischemic pain, while pressure pain thresholds are lower in F than M. There is strong evidence that F tolerate less thermal (heat, cold) and pressure pain than M but it is not the case for tolerance to ischemic pain, which is comparable in both sexes. The majority of the studies that measured pain intensity and unpleasantness showed no sex difference in many pain modalities. In summary, 10 years of laboratory research have not been successful in producing a clear and consistent pattern of sex differences in human pain sensitivity, even with the use of deep, tonic, long-lasting stimuli, which are known to better mimic clinical pain. Whether laboratory studies in healthy subjects are the best paradigm to investigate sex differences in pain perception is open to question and should be discussed with a view to enhancing the clinical relevance of these experiments and developing new research avenues.
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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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".