Does gender affect self-perceived pain in cancer patients? —A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pain is reported in approximately 50-70% of cancer patients. Studies on gender differences in perceived pain generally report lower pain thresholds and increased pain prevalence in women, which may be attributed to gender-specific behaviors, stereotypes, and unknown etiological factors. There are sparse and inconclusive results on gender differences in self-perceived pain in the cancer setting. The aim of this article was to examine the effect of gender on baseline perceived pain intensity in cancer patients through a meta-analysis. METHODS: A literature search was conducted using Ovid MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials [1947-2016] to identify observational studies and controlled trials that reported on gender-specific pain intensity in cancer patients. Using random-effects modeling, weighted mean differences and 95% confidence intervals (CI) were used to estimate the effect of gender on pain severity in cancer patients. A P value of less than 0.05 was considered statistically significant. RESULTS: Of the 1,911 search results reviewed, 13 studies were included. The weighted mean difference (95% CI) in pain intensity was as follows: -0.26 (95% CI: -0.57 to 0.04, P=0.09) for the 0-10 Numerical Rating Scale (NRS) group (n=3,752, 9 studies). When restricted to only patients with advanced cancer, the weighted mean difference was -0.08 (95% CI: -0.36 to 0.20, P=0.58) (n=2,762, 4 studies). The weighted mean difference in the Brief Pain Inventory scores between males and females was 0.03 (95% CI: -1.23 to 1.29, P=0.96) (n=521, 4 studies). CONCLUSIONS: Baseline perceived pain intensity in cancer patients did not significantly differ based on gender.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.060 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".