Kidney cancer survivorship care: Patient experiences in a Canadian setting
Bibliographic record
Abstract
INTRODUCTION: The incidence of kidney cancer (KCa) in Canada is rising. Despite this, there is a shortage of research assessing KCa care experiences. This study aims to explore the current experiences of KCa survivors related to treatment and management, information provision, and barriers to care. METHODS: A cross-sectional, descriptive study of KCa patients was conducted online and through various cancer centers across Canada. English- and French-speaking adults who received a KCa diagnosis and were currently undergoing treatment or had completed treatment in Canada were eligible to participate. RESULTS: In total, 368 surveys were completed. Ten percent of respondents had not yet received treatment, 29% were receiving treatment, and 56% had completed treatment. Most respondents (72%) had localized KCa (stage 0-3) at diagnosis. Sixty-one percent of respondents reported that their doctors discussed various treatment options with them and 24% reported discussing applicable clinical trials. Most (85%) respondents received information about their KCa and 36% discussed where to get information about their disease and support. The most commonly reported barriers to care were side effects (26%), system delays (26%), not having access to certain treatments (25%), and financial burden (24%). More participants in Central Region and Quebec (p=0.004) and rural/suburban (p=0.014) areas reported lacking access to certain treatments and KCa experts. CONCLUSIONS: This was the first large-scale study to explore access to care experiences of Canadian KCa survivors. Results show examples of good patient-centered care and provide new practical information that can inform efforts to improve patient-centered care for KCa patients.
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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 | 0.003 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".