The effect of selection and referral biases for the treatment of localised prostate cancer with surgery or radiation
Bibliographic record
Abstract
BACKGROUND: Consultation with radiation oncologists, in addition to urologists, is advocated for patients diagnosed with prostate cancer. Treatment patterns for patients receiving consultation from radiation oncologists in addition to urologists have not previously been described. METHODS: We conducted a matched cohort study of men with newly diagnosed non-metastatic prostate cancer in Ontario, Canada. Patients who underwent consultation with a radiation oncologist prior to treatment were matched 1:1 with patients managed by a urologist alone based on tumour and patient characteristics. We examined rates of active treatment (surgery or radiotherapy) within one year following diagnosis. RESULTS: Among 5708 matched pairs (11,416 patients), those who received radiation oncology consultation were more likely to undergo active treatments whether they had intermediate or high-risk disease (88.6% vs. 65.9%, p < 0.0001; adjusted odds ratio 4.0, 95% CI: 3.6-4.4) or low-risk disease (56.1% vs. 13.3%, p < 0.0001; adjusted odds ratio 8.4, 95% CI: 6.7-10.6). This effect persisted after considering age, comorbidity, tumour volume and year of diagnosis. CONCLUSIONS: Patients newly diagnosed with prostate cancer who receive radiation oncology consultation are associated with a higher rate of active treatment, compared to patients managed by urologists only. Selection and referral biases, and unmeasured confounding such as patient preference must be considered as important factors attributing this association.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".