Establishing achievable benchmarks for quality improvement in systemic therapy for early-stage breast cancer.
Bibliographic record
Abstract
263 Background: Setting realistic targets for performance on quality indicators (QI) is a consistent challenge in quality improvement. The purpose of this study was to utilize administrative data to define achievable targets for QI in the early stage breast cancer (EBC) population in relation to systemic therapy (ST) delivery based on best performers. Methods: Deterministically linked administrative healthcare databases were used to identify EBC cases diagnosed 2006 – 2010 in Ontario, Canada. Panel of previously established QIs for systemic therapy was applied to patients who met eligibility criteria for the individual indicators. Institutions with less than 10 eligible patients for a specific indicator were excluded. An empiric benchmark was defined as the proportion of patients meeting the indicator from institutions accounting for the top decile of eligible patients. Results: We identified 28,303 EBC patients who received surgery of which 12,252 received adjuvant chemotherapy. Benchmark results are summarized in Table. Conclusions: Many institutions fell considerably below the benchmark. Further analysis of institution-level drivers of high quality care is required to help characterize high performing institutions. [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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".