Determinants of waiting time from initial diagnostic procedure to surgery among women with localized breast cancer in Quebec, 1992-1997
Bibliographic record
Abstract
Background. The early diagnosis and treatment of breast cancer has become an important health care concern. A recent study reported the median waiting time for breast cancer surgery in Quebec was 34 days with 14% of women waiting in excess of 90 days. Understanding the determinants of long waiting is essential to develop optimum interventions to reduce delay. Objective. The purpose of this study was to identify the determinants of waiting time to surgery among women with primary breast cancer in Quebec between 1992 and 1997. Methods. The target population was all women 20 years and older diagnosed with primary breast cancer in Quebec between 1992 and 1997. The data was compiled from physician fee-for-service claims maintained by the Regie de I'assurance maladie du Quebec (RAMQ); the Quebec hospital discharge database (MedEcho), and the 1991 Canadian census. Waiting time was defined as the number of days from the initial breast diagnostic procedure to the first definitive surgical treatment. Three-level hierarchical linear models were used for statistical analysis. Findings . Overall, 13,383 women with primary breast cancer treated by 614 surgeons in 107 hospitals were identified. No statistically significant variation of waiting time was found among hospitals. Longer waiting times for breast cancer surgery were observed for women 50 to 64 years of age, without comorbidity, with history of benign breast disease, living in the lower education areas, having surgery at day-surgery setting, having surgery in more recent years, or having surgery performed by younger a surgeon (20 to 49 years old). Women who had surgery performed in a teaching hospital had longer waiting times and this effect was larger when mastectomy was performed. These results could be used to identify women and care delivery practices at higher risk for delays which could be the focus of interventions.
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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 | 0.001 | 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".