Radiotherapy wait times for patients with a diagnosis of invasive cancer, 1992-2000.
Bibliographic record
Abstract
PURPOSE: To study the wait times for cancer patients from the time of diagnosis to consultation with a radiation oncologist (T1), from consultation to radiotherapy (T2) and from diagnosis to radiotherapy (T3) in the context of treatment practices and measurement issues. METHODS: From 1992 to 2000, we studied 6585 Nova Scotian patients over the age of 24 years with a diagnosis of breast, lung, colorectal or prostate cancer who received radiotherapy within 1 year of diagnosis. Multivariate analyses examined associations between wait time and diagnosis year, age, sex, median household income (MHI), distance to the cancer centre and extent of disease. Univariate findings reported are median times and interquartile ranges. RESULTS: The T3 was 16 weeks for breast and colorectal cancer, 6 weeks for lung cancer and 18 weeks for prostate cancer. The greatest T1 decrease over time was for prostate cancer: 13-8 weeks (hazards ratio [HR] = 1.07, 95% confidence interval [CI] = 1.05-1.10). The T2 increased for all cancers, and the T3 increased from 5 to 7 weeks for lung cancer, from 17 to 22 weeks for prostate cancer and from 10 to 18 weeks for breast cancer, with no change for colorectal cancer. The T3 decreased by age for breast cancer (HR = 1.12, CI = 1.10-1.14) and prostate cancer (HR = 1.07, CI = 1.02-1.11), showed no consistent association with distance to a cancer centre and varied by extent of disease. Patients with localized lung disease had a longer T3 than those with distant disease, but the opposite results were noted for patients with breast cancer. The T3 was greater for regional than distant disease in lung and breast cancers. Sex and MHI had no effect. CONCLUSION: Wait times reflected clinical practice, and there were no adverse patterns related to age, sex, income or distance from a cancer centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".