Prostate Cancer Supportive Care (PCSC) Program: A model for meeting an unmet need for patients with PC.
Bibliographic record
Abstract
308 Background: Prostate cancer (PC) patients (pts) face treatment-related sequelae that affect their quality of life. The PCSC Program was created in 2013 and is located in the Vancouver Prostate Centre’s urology clinic. It is a clinical, educational, and evidence-based program that addresses challenges experienced by PC pts from the time of diagnosis onward. The program consists of 8 “modules,” each designed to support a specific concern. Methods: We describe the operational details of the PCSC Program and its metrics. Results: The program consists of a Medical Director and Program Manager who articulate the mission, oversee the day-to-day operations, and hire and manage the allied health clinicians and administrative staff. Clinicians include two sexual health RNs (1.4 FTE), two pelvic floor physiotherapists, a nurse practitioner, a registered dietitian, two clinical exercise physiologists, and two clinical counselors (all 0.2 FTE). A sexual health urologist is in the clinic, and a medical oncologist delivers the metastatic disease module. As of 09/2023, 3562 pts registered for this no-cost program (Table). Pts choose modules relevant to their interests and needs. The program supports free language interpretation services to non-English speaking pts. Translation of education session recordings is currently underway. In 4/2020, in response to the COVID-19 pandemic, the real-time group education sessions and clinic appointments transitioned from in-person to hybrid delivery, with virtual options enabling the participation of pts from throughout British Columbia. PCSC maintains high patient satisfaction, as indicated by responses to patient satisfaction questionnaires. Conclusions: The need for PC supportive care is well recognized but may be challenging when the services are fragmented, costly, and time-consuming to orchestrate. All modules offered by the PCSC Program can be accessed with one registration and are conveniently housed in one location, which also facilitates clinician communications across disciplines. We attribute the success of our program and high patient satisfaction to this model and our team approach to the individual patient’s needs. [Table: see text]
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".