Driving quality improvement with public reporting: Use of imaging tests outside guidelines for early-stage breast cancer in Ontario.
Bibliographic record
Abstract
193 Background: Most patients diagnosed with breast cancer will have early stage (stage I or II) disease, with low chance of distant metastases. Thus most guidelines, including Choosing Wisely, recommend against imaging tests for distant metastases in asymptomatic early stage breast cancer. Despite this, most (86%) of these patients in Ontario received these tests from which they are not likely to benefit and may result in investigations that can be invasive and delay treatment. Publicly reported indicators, such as those in Ontario’s Cancer System Quality Index (CSQI), can bring research findings to action by identifying areas for improvement and facilitating ongoing assessment. In practice, this can be challenging due to limitations in administrative data. Moreover, relatively few quality improvement indicators focus on efficiency, the dimension of quality looking at best use of resources to achieve desired outcomes. We sought to examine trends in the use of imaging tests in early stage breast cancer and to drive quality improvement efforts via public reporting. Methods: Data from the Ontario Cancer Registry, the Discharge Abstract Database and the Ontario Health Insurance database was used to identify how many Ontario breast cancer patients diagnosed with early stage breast cancer received staging tests from 2012–2014. Imaging tests included were ultrasound, CT scan, MRI, x-ray and bone scan. The results were subsequently shared with the Regional Cancer Centres and publically released in the CSQI. Results: From 2012 to 2014, 75.1, 72.7 and 71.3% respectively of early stage breast cancer patients received at least one imaging test for staging. This is much higher than the 5-10% of patients expected to need tests due to symptoms or comorbidities. While the regional variation ranged from 47-80%, the rates were high across the province with no clear pattern. Conclusions: Public reporting may be having some effect on overtesting, but rates remain high. Following outreach by the Cancer Quality Council of Ontario and Cancer Care Ontario, targeted regional interventions are being developed and implemented, the impact of which will be assessed and reported in future releases of the CSQI.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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 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".