Setting quality improvement priorities for women receiving systemic therapy (ST) for early stage breast cancer (EBC) using population level administrative data.
Bibliographic record
Abstract
299 Background: Routine evaluation of evidence informed quality measures (QM) can drive improvement in cancer systems by highlighting potential gaps in care. Targeting quality improvement at QMs that demonstrate substantial variation has the potential to make the largest impact on quality at a population level. We aimed to use variation in performance to set priorities for improving the quality of ST for women with EBC. Methods: EBC cases diagnosed 2006 – 2010 in Ontario, Canada were identified in the Ontario Cancer Registry and linked deterministically to multiple health care databases. A panel of QMs, previously developed to be operationalized for administrative data, was applied to reflect the quality of ST. Each QM was evaluated in all patients who met the inclusion criteria for the individual measure. QMs were ranked based on institutional variation in performance using the mean absolute difference (MAD). Results: We identified 28,303 patients, treated at 84 institutions. The performance of each QM is listed in Table 1. Timely receipt of ST, febrile neutropenia (FN) secondary prophylaxis, emergency room visits or hospitalizations, receipt of hormonal therapy (HT) and the use of surveillance imaging represented the 5 QM that demonstrated the greatest variation. Conclusions: Considerable institutional-level variation highlights potentially actionable areas of improvement [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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| 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".