Survivorship care in Canadian genitourinary oncology: Toward a multidisciplinary perspective.
Bibliographic record
Abstract
337 Background: Genitourinary (GU) cancer survivors comprise nearly 25% of cancer survivors and >50% of all male survivors. To further improve care of this patient population, we need to better understand physician specialist perceptions of survivorship care. Methods: All medical oncologists, urologists and radiation oncologists treating GU cancers in Canada were surveyed with a web−based questionnaire. A total of 27 multiple choice and Likert scale questions were developed in 5 domains: i) demographics, ii) current post cancer treatment care practices, iii) perceived barriers, iv) accessibility to survivorship resources and v) perceptions of advocacy groups. Participants were identified through their respective professional associations. Results: There were 306 responses and 260 were eligible for analysis; 45 medical oncologists, 125 urologists and 90 radiation oncologists. A total of 56% of physicians discharged GU cancer survivor follow-up care to a primary care practitioner (PCP) at some point after treatment. Compared to urologists and radiation oncologists, medical oncologists were least likely to share care with a PCP (4%). Only 47% of all physicians consistently provided a written follow up plan to the PCP and only 25% of these provided lifestyle recommendations. Lack of time and resources were the most commonly reported barriers. About half of physicians reported access to cancer rehabilitation programs as difficult and British Columbia was the most frequently cited region without access. Medical oncologists compared to other subspecialties accessed genetic counseling more easily while access to pain management programs was cited as the most difficult. In general, physicians in community hospitals had the most difficulties. Utilization of advocacy groups was limited, e.g. 23% for prostate cancer. The most underutilized advocacy group was for testis cancer (4%). Conclusions: To our knowledge this is the first study to address the challenges of GU cancer survivorship care in Canada. The barriers to care and underutilization of advocacy groups quoted in this study may be used to stimulate nationwide initiatives to further long-term strategies in GU survivorship management planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".