Factors Influencing the Quality of Local Management of Ductal Carcinoma In Situ: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend radiotherapy (rt) after breast-conserving surgery (bcs) for optimal control of ductal carcinoma in situ (dcis). The aim of the present study was to characterize the rates of rt consideration and administration, and to identify factors influencing those rates in a cohort of women diagnosed between 1998 and 2005 in Quebec. METHODS: Quebec's medical service claims and discharge abstract database were used. Using consultation for rt as an indicator for rt consideration, odds ratios (ors) and 95% confidence intervals (cis) were estimated using a generalized estimating equations regression model. RESULTS: Of 4139 women analyzed (mean age: 58 years), 3435 (83%) received a consultation for rt, and 3057 of them (89%) proceeded with treatment. The rate of rt consideration increased by 7.1% over the study period, with notable differences in the various age groups. Relative to women 50-69 years of age, the ors for being considered for rt were, respectively, 0.89 (95% ci: 0.71 to 1.12), 0.71 (95% ci: 0.55 to 0.92), and 0.20 (95% ci: 0.14 to 0.31) for women younger than 50, 70-79, and 80 years of age and older. Distance to a designated breast care centre lowered the probability of rt consideration, but the presence of comorbidities did not. A surgeon's volume of bcss increased the probability of being considered for rt by 7% for every 10 such procedures performed (or: 1.07; 95% ci: 1.04 to 1.11). CONCLUSIONS: Consideration for rt has increased over time. However, older women (despite being in good health) and those living far from a designated breast care centre or having a low-case-volume surgeon were less likely to be considered for rt.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".