Treatment variation in patients diagnosed with early stage breast cancer in Alberta from 2002 to 2010: a population-based study
Bibliographic record
Abstract
BACKGROUND: Breast-conserving surgery (BCS) followed by radiotherapy is generally the preferred treatment for women diagnosed with early stage breast cancer. This study aimed to investigate the proportion of patients who receive BCS versus mastectomy and post-BCS radiotherapy, and explore factors associated with receipt of these treatments in Alberta, Canada. METHODS: A retrospective population-based study was conducted that including all patients surgically treated with stage I-III breast cancer diagnosed in Alberta from 2002-2010. Clinical characteristics, treatment information and patient age at diagnosis were collected from the Alberta Cancer Registry. Log binomial multiple regression was used to calculate stage-specific relative risk estimates of receiving BCS and post-BCS radiotherapy. RESULTS: Of the 14 646 patients included in the study, 44% received BCS, and of those, 88% received post-BCS radiotherapy. The adjusted relative risk of BCS was highest in Calgary and lowest in Central Alberta for all disease stages. Relative to surgeries performed in Calgary, those performed in Central Alberta were significantly less likely to be BCS for stage I (RR = 0.65; 95% 0.57, 0.72), II (RR = 0.58; 95% 0.49, 0.68), and III (RR = 0.62; 95% CI: 0.37, 0.95) disease, respectively, adjusting for patient age at diagnosis, clinical and treatment characteristics. No significant variation of post-BCS radiotherapy was found. CONCLUSIONS: Factors such as region of surgical treatment should not be related to the receipt of standard care within a publicly-funded health care system. Further investigation is needed to understand the significant geographic variation present within the province in order to identify appropriate 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".