Bibliographic record
Abstract
PURPOSE OF REVIEW: The considerable prevalence of sexual health problems in men after cancer treatment coupled with the severity of impact and challenges to successful intervention make sexual dysfunction one of the most substantial health-related quality of life burdens in all of cancer survivorship. Surgeries, radiation therapies, and nontreatment (e.g., active surveillance) variously result in physical disfigurement, pain, and disruptions in physiological, psychological, and relational functioning. Although biomedical and psychological interventions have independently shown benefit, long-term, effective treatment for sexual dysfunction remains elusive. RECENT FINDINGS: Recognizing the complex nature of men's sexual health in an oncology setting, there is a trend toward the adoption of a biopsychosocial orientation that emphasizes the active participation of the partner, and a broad-spectrum medical, psychological, and social approach. Intervention research to date provides good insight into the potential active ingredients of successful sexual rehabilitation programming. SUMMARY: Combining a biopsychosocial approach with these active intervention elements forecasts an optimistic future for men's sexual rehabilitation programming within oncology. However, significant gaps remain in our understanding of patient experience and appropriate sexual health intervention for gay men and men of diverse race and culture.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".