Factors associated with wait times across the breast cancer treatment pathway in Ontario
Bibliographic record
Abstract
BACKGROUND: Longer times from diagnosis to breast cancer treatment are associated with poorer prognosis. This study examined factors associated with wait times by phase in the breast cancer treatment pathway. METHODS: There were 1760 women eligible for the study, aged 50-69 diagnosed in Ontario with invasive breast cancer from 1995-2003. Multivariate logistic regression examined factors associated with greater than median wait times for each phase of the treatment pathway; from diagnosis to definitive surgery; from final surgery to radiotherapy without chemotherapy and from final surgery to chemotherapy. RESULTS: The median wait times were 17 days (Inter Quartile Range (IQR) = 0-31) from diagnosis to definitive surgery, 44 days (IQR = 34-56) from final surgery to postoperative chemotherapy and 75 days (IQR = 57-97) from final surgery to postoperative radiotherapy. Diagnosis during 2000-2003 compared to 1995-1999 was associated with significantly longer wait times for each phase of the treatment pathway. Higher income quintile was associated with longer wait time from diagnosis to surgery (OR = 1.47, 95% CI = 1.05-2.06) and shorter wait times from final surgery to radiotherapy (OR = 0.60, 95% CI = 0.37-0.96). Greater stage at diagnosis was associated with shorter wait times from diagnosis to definitive surgery (stage III vs I: OR = 0.49, 95% CI = 0.34-0.71). CONCLUSIONS: While diagnosis during the latter part of the study period was associated with significantly longer wait times for all phases of the treatment pathway, there were variations in the associations of stage and income quintile with wait times by treatment phase. Continued assessment of factors associated with wait times across the breast cancer treatment pathway is important, as they indicate areas to be targeted for quality improvement with the ultimate goal of improving prognosis.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".