Treatment decision-making in men with localized prostate cancer living in remote area: A cross-sectional observational study
Bibliographic record
Abstract
INTRODUCTION: For the management of localized prostate cancer, patient treatment choice is poorly documented among people living in remote areas, where access to certain treatments offered in large centers involves travelling several hundred kilometres. This study aimed to describe and identify the determinants of treatment decision-making in men with localized prostate cancer living in remote areas. METHODS: In this cross-sectional study, patients with prostate cancer were recruited from Rouyn-Noranda's urology clinic (Quebec, Canada) between 2017 and 2019. RESULTS: A total of 127 men (mean age 68.34±7.23 years) constituted the study sample. Radiotherapy, a treatment not available locally, was chosen most frequently (67.7%), followed by options available locally, such as surgery (22.8%) and active surveillance (9.4%). Most patients preferred to play an active role in this choice (53.5%) and agreed with the statement, "I chose that treatment because it gives the best chance for a cure" (86.6%). Multiple logistic regression analysis revealed that cancer stage (odds ratio [OR] 10.15; 95% confidence interval [CI] 3.18-32.40) was the only factor associated with radiotherapy choice (patients with lower stage cancer were more likely to choose radiotherapy). The socioeconomic status was not associated with treatment choice. CONCLUSIONS: While radiotherapy was not available locally, it was the most frequently chosen treatment, even though the available literature suggests that no one treatment option is superior in terms of cancer control. The choice of radiotherapy is not associated with patient income, but rather the cancer stage. This result could be explained by the patients' desire to avoid surgery and its adverse effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".