Analysis of Treatment Practices for Elderly Cancer Patients in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: Older patients are underrepresented in many areas of cancer services utilization and in clinical trial enrollment. This study evaluates whether age, when adjusted for sex, comorbidity, stage, tumor site, geography, and time period, is predictive of cancer treatment practice. METHODS: First, we used the Ontario Cancer Registry (OCR) to examine for any apparent differences in treatment practices between elderly (> or = 70 years) and younger patients in the last three decades. Second, we performed a chart review of 1,505 patients with lung, breast, and colorectal cancers seen in Ontario either at an urban center, the Princess Margaret Hospital, or at a rural center, the Northwestern Regional Cancer Centre. Patients were randomly selected from two time periods, 1977 to 1978 and 1997; and the study population was to comprise at least 50% elderly patients. RESULTS: OCR data demonstrated that, in some settings, such as colorectal cancer, the proportions of elderly cancer patients who were referred to cancer centers and who received any cancer treatment were lower than their younger counterparts. The chart review data showed that increasing age was a significant negative predictor for receiving any cancer treatment (P < .001, multivariate analysis) and for having a clinical trial discussion with the treating specialist (P < .001, multivariate analysis). CONCLUSION: Independent of other factors, older age is consistently a cause of disparity in cancer treatment practice and in clinical trial discussion with patients. By increasing the accrual rate of elderly cancer patients in clinical trials, a better understanding of appropriate therapies for this patient population can be obtained and may, thereby, impact on their cancer-related morbidity and mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".