Bibliographic record
Abstract
INTRODUCTION: For men with cancer, sexual dysfunction is a common issue and has a negative impact on quality of life, regardless of whether he has a partner. In general, sexuality encompasses much more than intercourse; it involves body image, identity, romantic and sexual attraction, and sexual thoughts and fantasies. AIM: Acknowledging that cancer affects multiple physical and psychosocial domains in patients, the authors propose that such changes also inform sexual function for the male survivor. METHODS: An in-depth review of the literature describing alterations to sexual functioning in men with cancer was undertaken. Based on this and the clinical expertise of the authors, a new model was created and is presented. RESULTS: This biopsychosocial model is intended to expand the understanding of male sexuality beyond a purely biomedical model that addresses dysfunction as distinct from the context of a man's life and sexual identity. CONCLUSION: Most data on sexual dysfunction in men with cancer are derived from those with a history of prostate cancer, although other data suggest that men with other types of malignancies are similarly affected. Unfortunately, male sexuality is often reduced to aspects of erection and performance. Acknowledging that cancer affects multiple physical and psychosocial domains in patients, the authors propose that such changes also inform sexual function for the male survivor. This biopsychosocial model might form the basis for interventions for sexual problems after cancer that includes a man and his partner as a complex whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".