Identifying opportunities to improve quality of cancer care: An evaluation of the use of routine surveillance imaging scans in women with early breast cancer (EBC) treated in Ontario, Canada.
Bibliographic record
Abstract
115 Background: Over use of routine surveillance imaging to detect recurrence in women with EBC was recently identified as one of the top five opportunities to improve the quality of cancer care by the American Society of Clinical Oncology. We undertook a population level assessment of the current practice of surveillance imaging in EBC women treated in Ontario Canada. Methods: Incident EBC patients diagnosed 01/07 – 12/09 in Ontario, Canada were identified from the Ontario Cancer Registry. Patient records were linked deterministically to provincial health care databases to provide comprehensive medical follow-up. Basic (chest x-ray, abdominal x-ray and abdominal ultrasound) and advanced (computed tomography [CT] or bone scans) surveillance imaging scans completed during the first and second year of follow-up (starting 6 months after surgery or upon completion of chemotherapy) were identified. Logistic regression models were used to identify covariates associated with advanced surveillance imaging. Results: 16,981 EBC patients were included in the analysis of which 7,907(46.6%) received chemotherapy. In the first year of follow up care, 8,311 (48.9%) had at least 1 basic imaging test, while 4,916 (29.0%) had advanced imaging. This fell to 45.7% (basic) and 25.1% (advanced) in the second year of follow-up. Bone scans were the most common advanced imaging test (14.5%), followed by CT thorax (10.8%). On multivariable analysis age, stage, the use of chemotherapy and comorbidity were associated with increased use of advanced surveillance imaging (Table). Conclusions: Surveillance imaging was common in the first two years of follow-up for EBC patients. While appropriate for symptom driven investigation, the high rate of advanced imaging scans suggests an opportunity for improvement with the Ontario cancer system. [Table: see text]
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".