Patterns and predictors of attendance at a comprehensive, multi-disciplinary supportive care program for men with prostate cancer and their partners.
Bibliographic record
Abstract
6 Background: Men diagnosed with prostate cancer (PC) face treatment-related sequlae that affect their health and quality of life. The Vancouver Prostate Centre’s (VPCs) Prostate Cancer Supportive Care (PCSC) Program is a comprehensive program for men and their partners that aims to address these challenges. Our objective is to examine registration rates, and the timing/intensity of follow-up with the program, and to explore clinical/sociodemographic factors associated with participation and non-participation. Methods: We used charts for all men who registered with the PCSC program (“registrants”), and a random sample of men who sought PC-related care at the VPC but did not register with the program (“non-registrants”) from Jan 2013-Dec 2016. Registrants were classified as “attenders” (came to PCSC information session/clinic visit), or “non-attenders” (did not attend). We used multivariate logistic regression to quantify the effect of diagnostic, treatment and sociodemographic characteristics on registration. We produced an unadjusted Kaplan-Meier estimator to assess the probability of program attendance over the disease trajectory for those who registered. We used Cox proportional hazards regression to examine impact of the same factors on timing of program participation and a binary logistic model to examine which factors impact program attendance. Results: Preliminary results suggest that 17% of the men who enroll in the program do not subsequently use any services. Program participation continues for more than four years after diagnosis and varies based on Gleason score (Chi Square (CS) = 20.9, p = 0.01), risk score (CS = 11.5, p = 0.02), and clinical T stage (CS = 14.0, p < 0.001). We found no difference participation by age, age at diagnosis, travel distance to clinic or treatment modality. Complete results will be available at the time of presentation. Conclusions: One in six men who register for supportive care do not end up using any despite the program being free of charge. Drivers of non-participation appear to be clinical, with lower risk patients being more likely to chose not to participate, though further investigation is required.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".