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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".