Is there age bias in the treatment of localized prostate carcinoma?
Bibliographic record
Abstract
BACKGROUND: Treatment recommendations for localized prostate carcinoma are based on the patient's remaining life expectancy (RLE), which is influenced by age, comorbidity, and tumor grade. Previous studies have evaluated the influence of age and comorbidity, but to the authors' knowledge not RLE, on actual treatment decisions. METHODS: An age-stratified random sample of 347 patients was generated from a cohort of all patients with newly diagnosed prostate carcinoma in the Ontario Cancer Registry between May 1, 1995 and April 30, 1996 (n = 5192). Chart review was performed to obtain detailed tumor, comorbidity, and treatment information. RLE was estimated from a published model derived from a cohort of 451 men with untreated prostate carcinoma who were followed for 15 years. Multivariable logistic regression was performed to evaluate predictors of treatment, such as radical prostatectomy (RP), radiotherapy (RT), or potentially curative therapy (RP or RT), in relation to patient age, comorbidity, tumor characteristics, and RLE. RESULTS: RP was provided within 6 months of diagnosis to 58.7%, 32.1%, 2.6%, and 0% of patients of ages < 60 years, 60-69 years, 70-79 years, and 80+ years, respectively. The results for RT were 6.4%, 30.9%, 23.4%, and 3.3%, respectively. Increasing comorbidity decreased rates of RP but did not affect use of RT. After controlling for comorbidity and tumor characteristics, older men were found to be treated with RP less often than younger men with similar RLE, whereas RLE did not appear to influence receipt of RT. CONCLUSIONS: Although different mechanisms may account for these results, an age bias may be present among urologists and radiation oncologists treating men with localized prostate carcinoma.
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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 | 0.044 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".