Factors influencing treatment decisions in patients with low risk prostate cancer referred to a brachytherapy clinic.
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to analyze factors influencing treatment decisions in patients diagnosed with low risk prostate cancer who were referred to a brachytherapy clinic and had to choose from four treatment options: expectant management (watchful waiting), radical prostatectomy, external beam radiation therapy, and permanent seed brachytherapy. METHODS: We analyzed factors that influenced the treatment decisions of 110 consecutive patients with low risk prostate cancer who were referred to a brachytherapy clinic in a hospital in Montreal, Canada. These factors included patient age, marital status, and profession, as well as referral source (a urologist or a radiation oncologist), and distance and driving time from the patient's home to the medical center. Cost was not a factor as the procedure is covered under the Canadian healthcare system. RESULTS: Of the 110 patients, 53 patients (48.2%) chose permanent seed brachytherapy, 33 patients (31.8%) chose expectant management, 12 patients (10.9%) chose external beam radiation therapy, and 10 patients (9.1%) chose radical prostatectomy. Patients who chose brachytherapy were significantly younger than those who chose external beam radiation therapy (p = 0.011). Patients living further away from the hospital than the median distance of 19.85 miles were more likely to choose brachytherapy than expectant management (p = 0.017).
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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 | 0.000 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".