Factors Associated With Pattern of Care Before Surgery for Breast Cancer in Quebec Between 1992 and 1997
Bibliographic record
Abstract
BACKGROUND: Practice guidelines for breast cancer emphasize the importance of establishing an accurate diagnosis using a minimum number of procedures and selecting optimal treatment regimens. Understanding the determinants of waiting time is essential to develop optimum interventions to reduce delay. OBJECTIVES: The purpose of this study is to estimate the extent to which variability in 1) the number of procedures before surgery and 2) waiting time from initial procedure to surgery are explainable by factors related to the woman, to the provider, and to the care setting. RESEARCH DESIGN: Records of physicians' fee-for-service claims were obtained for 23,370 women undergoing breast cancer surgery in Quebec between 1992 and 1997. Multilevel logistic regression was used to determine predictors of having multiple procedures before surgery. Hierarchical linear regression models were used to identify predictors of waiting time, separately for women with lymph node involvement and without this involvement. RESULTS: Overall, 23% of the women had 3 or more procedures before surgery with significant variation found across hospitals and surgeons. Number of procedures was a strong predictor of waiting time. Waiting time also varied by stage, age, comorbidity, a history of benign disease, surgical setting, calendar time, month of initial procedure, and hospital teaching status. CONCLUSION: Although variability in waiting time was more strongly influenced by the characteristics of the women rather than by physician- or hospital-related factors, most variation remained unexplained by the factors included in this study. To reduce overall waiting time, strategies would need to be systemically applied.
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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) | 0.001 | 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".