The Need for Survivorship Care in Genitourinary Cancers: Considerations from SUO and LUGPA
Bibliographic record
Abstract
INTRODUCTION: A quarter of American cancer survivors have genitourinary malignancies that are largely managed by urologists. We explored urologist perceptions about survivorship care for genitourinary malignancies. METHODS: A total of 701 SUO (Society of Urologic Oncology) and 1,746 LUGPA (Large Urology Group Practice Association) members were invited to complete a web based survey composed of 5 domains, including 1) demographics, 2) current survivorship care practices, 3) perceived barriers, 4) accessibility to survivorship resources and 5) perceptions of advocacy groups. RESULTS: Of 191 respondents 137 (72%) had no training in survivorship care. Of the 174 respondents 129 (74%) practiced shared care models while 45 (26%) preferred pure specialized followup care. Only 39 of 129 respondents (30%) with a shared care model always provided a written care plan. These plans infrequently included information on lifestyle modifications and educational resources. Routine patient referral to advocacy organizations was highest for prostate cancer at 40% followed by bladder, testicular and kidney cancers at 17%, 10% and 8%, respectively. Lack of time/resources and practice guidelines were considered the 2 most important barriers to survivorship care by 31% and 30% of participants, respectively. Web based information on advocacy groups and best practice guidelines were selected as the most important initiatives to promote survivorship care. CONCLUSIONS: Despite the low response rate this study highlights important practice gaps in survivorship care for patients with genitourinary malignancies. In collaboration with advocacy organizations professional societies should initiate programs to better educate and train their members in survivorship care guidelines and consensus best practices.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".