Identifying opportunities to improve quality of cancer care: An evaluation of the use of diagnostic imaging in women curatively treated for early breast cancer (EBC).
Bibliographic record
Abstract
6603 Background: The overuse of imaging scans to detect recurrence in curatively treated EBC patients was recently identified as one of ASCO’s top five opportunities to improve the quality of cancer care. We undertook a population-level assessment of the current practice of imaging in women treated for EBC. Methods: EBC patients diagnosed in Ontario, Canada between 01/2006 –and 12/2010 were identified from the Ontario Cancer Registry. Patient records were linked deterministically to provincial healthcare databases to provide comprehensive follow-up. We identified any advanced imaging scans (AIS) (computed tomography, bone scans) and basic imaging scans (BIS) during the first year after completion of curative treatment. Descriptive analyses were used to assess the impact of patient and provider characteristics on the likelihood of having AIS. Results: Of the 30,006 EBC patients included, 9,186 (30.6 %) had AIS in year one (Table). In patients with AIS, the median number of scans was 2.5(IQR1-3). Older age, higher stage, comorbidity, chemotherapy (CT) exposure and having had staging investigations increased the likelihood of AIS (P < 0.001). The majority of year one scans were ordered by medical oncologists (38%) followed by primary care physicians (23%), surgeons (13%) and emergency room physicians (7%). Conclusions: While imaging is common in follow-up for EBC patients and appropriate for symptom driven investigation, the high rate of AIS use is an actionable target for quality improvement. Use of BIS and AIS in first year of follow up for EBC. Advanced N=9,186 (30.6%) Basic N=8,401 (28.0%) No Imaging N=12,419 (41.4%) Age Median (IQR) 60 (50-71) 60 (49-70) 61 (51-70) Stage 1 3,127 (34.0%) 4,018 (47.8%) 6,123 (49.3%) 2 3,978 (43.3%) 3,421 (40.7%) 4,888 (39.4%) 3 2,081 (22.7%) 962 (11.5%) 1,408 (11.3%) Comorbidity 0 8,333 (90.7%) 7,780 (92.6%) 11,695 (94.2%) 1 268 (2.9%) 183 (2.2%) 203 (1.6%) 2+ 585 (6.4%) 438 (5.2%) 521 (4.2%) Treatment Adjuvant CT 4,392 (47.8%) 3,441 (41.0%) 4,529 (36.5%) Neoadjuvant CT 743 (8.1%) 303 (3.6%) 533 (4.3%) No CT 4,051 (44.1%) 4,657 (55.4%) 7,357 (59.2%) Staging Scans 6,650 (72.4%) 5,237 (62.3%) 7,261 (58.5%)
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".