Waiting times for radiation therapy in Ontario.
Bibliographic record
Abstract
BACKGROUND: The mass media and clinical journals have reported lengthy waiting times after surgery before initiation of radiation therapy (RT) for cancer across Canada. We aimed to describe the length of time between the last date of surgery or biopsy or chemotherapy and first date of RT. METHODS: This is a population-based study measuring waiting times for RT in Ontario among all patients with potentially curable cancer of the cervix, tonsil and larynx and a random sample of women who had had breast cancer resection, whose first date of RT fell between Sept. 1, 2001, and Aug. 31, 2002. Abstraction of original health care records provided each patient's demographics, cancer stage and cancer treatment (last surgery, consultation, simulation, first RT). Last dates of chemotherapy before RT were obtained from abstraction or from Ontario Health Insurance Plan (OHIP) files, and last dates of surgery before RT were compared with dates in the Canadian Institute for Health Information (CIHI) Discharge Abstract Database. RESULTS: Waiting times between the last date of surgery or chemotherapy and the first date of RT varied significantly among the health regions of Ontario. Increasing age, but not the presence of comorbidity, was associated with longer waiting times. Women who did not receive postoperative chemotherapy before RT for breast cancer waited significantly longer than all others. CONCLUSION: Measurement of waiting times for cancer RT must discount time during which adjuvant intravenous chemotherapy is administered after surgery and before RT. There appears to be a formal or informal process by which those at highest risk begin RT most rapidly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".