Canadian consensus algorithm for erectile rehabilitation following prostate cancer treatment
Bibliographic record
Abstract
INTRODUCTION: The present descriptive analysis carried out by a pan-Canadian panel of expert healthcare practitioners (HCPs) summarizes best practices for erectile rehabilitation following prostate cancer (PCa) treatment. This algorithm was designed to support an online sexual health and rehabilitation e-clinic (SHARe-Clinic), which provides biomedical guidance and supportive care to Canadian men recovering from PCa treatment. The implications of the algorithm may be used inform clinical practice in community settings. METHODS: Men's sexual health experts convened for the TrueNTH Sexual Health and Rehabilitation Initiative Consensus Meeting to address concerns regarding erectile dysfunction (ED) therapy and management following treatment for PCa. The meeting brought together experts from across Canada for a discussion of current practices, latest evidence-based literature review, and patient interviews. RESULTS: An algorithm for ED treatment following PCa treatment is presented that accounts for treatment received (surgery or radiation), degree of nerve-sparing, and level of pro-erectile treatment invasiveness based on patient and partner values. This algorithm provides an approach from both a biomedical and psychosocial focus that is tailored to the patient/partner presentation. Regular sexual activity is recommended, and the importance of partner involvement in the treatment decision-making process is highlighted, including the management of partner sexual concerns. CONCLUSIONS: The algorithm proposed by expert consensus considers important factors like the type of PCa treatment, the timeline of erectile recovery, and patient values, with the goal of becoming a nationwide standard for erectile rehabilitation following PCa treatment.
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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 | 0.027 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".