Imaging for distant metastases in women with early-stage breast cancer: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Practice guidelines recommend that imaging to detect metastatic disease not be performed in the majority of patients with early-stage breast cancer who are asymptomatic. We aimed to determine whether practice patterns in Ontario conform with these recommendations. METHODS: We used provincial registry data to identify a population-based cohort of Ontario women in whom early-stage, operable breast cancer was diagnosed between 2007 and 2012. We then determined whether imaging of the skeleton, thorax, and abdomen or pelvis had been performed within 3 months of tissue diagnosis. We calculated rates of confirmatory imaging of the same body site. RESULTS: Of 26,547 patients with early-stage disease, 22,811 (85.9%) had at least one imaging test, and a total of 83,249 imaging tests were performed (mean of 3.7 imaging tests per patient imaged). Among patients with pathologic stage I and II disease, imaging was performed in 79.6% (10,921/13,724) and 92.7% (11,882/12,823) of cases, respectively. Of all imaging tests, 19,784 (23.8%) were classified as confirmatory investigations. Imaging was more likely for patients who were younger, had greater comorbidity, had tumours of higher grade or stage or had undergone preoperative breast ultrasonography, mastectomy or surgery in the community setting. INTERPRETATION: Despite recommendations from multiple international guidelines, most Ontario women with early-stage breast cancer underwent imaging to detect distant metastases. Inappropriate imaging in asymptomatic patients with early-stage disease is costly and may lead to harm. The use of population datasets will allow investigators to evaluate whether or not strategies to implement practice guidelines lead to meaningful and sustained change in physician practice.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".