Prevalence of erectile dysfunction in male survivors of cancer: a systematic review and meta-analysis of cross-sectional studies
Bibliographic record
Abstract
BACKGROUND: Prevalence of erectile dysfunction (ED) in male survivors of cancer across cancer types has not been systematically analysed. AIM: To estimate the prevalence of ED in all types of cancer and identify characteristics associated with ED in survivors of cancer. DESIGN AND SETTING: Systematic review and meta-analysis (MA) of cross-sectional studies. METHOD: MEDLINE, CINAHL, PsycINFO, and EMBASE were searched, targeting reports published from inception to 1 February 2020. All retrospective or prospective studies reporting prevalence of ED in male patients with cancer and using a validated tool for detection of ED were included. A random-effects MA model was used to pool prevalence of ED as absolute estimates at three different stages, that is, 'healthy', 'at diagnosis', and 'after treatment'. A univariate MA regression including the three-level group variable as the only independent variable was used to assess the difference in ED prevalence across the three groups. Further MAs were conducted for studies involving patients at diagnosis and after treatment, and statistical inferences were made with setting for multiple testing controlling for a false discovery rate (FDR) <0.05. RESULTS: In total, 1301 studies were assessed for inclusion. Of these, 141 were potentially eligible and subsequently scrutinised in full text. Finally, 43 studies were included with a total of 13 148 participants. Overall, pooled data of the included studies showed an ED prevalence of 40.72% (95% confidence interval [CI] = 31.80 to 50.29) in patients with cancer, with prevalences of 28.60% (95% CI = 12.10 to 53.83) at time of diagnosis and 42.70% (95% CI = 32.97 to 53.03) after treatment, with significant difference between these two stages and across cancer locations, controlling for an FDR <0.05. CONCLUSION: Erectile dysfunction was particularly high in male survivors of cancer and was associated with cancer treatment, cancer site, and age.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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 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".