Factors Associated with Imaging in Patients with Early Breast Cancer After Initial Treatment
Bibliographic record
Abstract
Background: Overuse of surveillance imaging in patients after curative treatment for early breast cancer (EBC) was recently identified as one of the Choosing Wisely Canada initiatives to improve the quality of cancer care. We undertook a population-level examination of imaging practices in Ontario as they existed before the launch of that initiative. Methods: Patients diagnosed with ebc between 2006 and 2010 in Ontario were identified from the Ontario Cancer Registry. Records were linked deterministically to provincial health care databases to obtain comprehensive follow-up. We identified all advanced imaging exams [aies: computed tomography (CT), bone scan, positron-emission tomography] and basic imaging exams (bies: ultrasonography, chest radiography) occurring within the first 2 years after curative treatment. Poisson regression was used to assess associations between patient or provider characteristics and the rate of AIES. Results: Of 30,006 women with ebc, 58.6% received at least 1 BIE, and 30.6% received at least 1 AIE in year 1 after treatment. In year 2, 52.7% received at least 1 BIE, and 25.7% received at least 1 AIE. The most common AIES were chest CTS and bone scans. The rate of AIES increased with older age, higher disease stage, comorbidity, chemotherapy exposure, and prior staging investigations (p < 0.001). Imaging was ordered mainly by medical oncologists (38%), followed by primary care physicians (23%), surgeons (13%), and emergency room physicians (7%). Conclusions: Despite recommendations against its use, imaging is common in EBC survivors. Understanding the factors associated with aie use helps to identify areas for further research and is required to lower imaging rates and to improve survivorship care.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".