Waiting Time for Radiation Therapy in Breast Cancer Patients in Quebec from 1992 to 1998: A study of surgically treated breast cancer patients in Quebec documents and helps to explain increased waiting times for radiation therapy.
Bibliographic record
Abstract
BACKGROUND : This study was conducted among surgically treated breast cancer patients in Quebec to determine waiting time between surgery and post-operative radiation therapy and factors influencing it. METHODS : Records of fee-for-service claims and hospitalizations were obtained for all women who, between 1992 and 1998, underwent an invasive procedure for non-metastatic breast cancer. Waiting time was defined as the time between either the last surgical procedure or the last cycle of chemotherapy and the initiation of radiation therapy. Hierarchical linear regression models were used to identify predictors of waiting time. RESULTS : Over seven years, 29,072 episodes of breast cancer treatments were identified, of which 17,684 included radiation therapy. The number of cases increased by 5.5% per year, but concurrent broadening of indications for radiation therapy led to an increase in the number of breast cancer patients receiving radiation therapy of 9% per year. In hierarchical linear modelling, comparing 1998 to 1992, median waiting time increased by 63% (95% confidence interval [CI] 35%–97%) in patients not receiving chemotherapy and by 35% (95% CI 3%–88%) in patients receiving chemotherapy. Other predictors of shorter waiting times were localized cancer stage, breast-conserving surgery, early consultation with a radiation oncologist, having surgery in a centre with a radiation therapy facility, living close to a radiation therapy facility and living in a higher socio-economic area. INTERPRETATION : Using administrative databases to evaluate waiting times is feasible. Explanations of the increased waiting time include increased demand, insufficient resources and changes in the indications for breast-conserving surgery and radiation therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".