Establishing achievable benchmarks for quality improvement in systemic therapy for early‐stage breast cancer
Bibliographic record
Abstract
BACKGROUND: Setting realistic targets for performance is a consistent challenge in quality improvement. In the current study, the authors used administrative data to define achievable targets for a panel of 15 previously developed quality indicators (QIs) focusing on systemic therapy in patients with early-stage breast cancer. METHODS: Deterministically linked administrative databases were used to identify patients with TNM stage I to stage III breast cancer who were diagnosed between 2006 and 2010 in Ontario, Canada. For each individual indicator, data-driven empirical benchmarks were calculated using the pared-mean benchmark approach. Variation in institution-level performance for each indicator was examined through the construction of funnel plots. RESULTS: A total of 28,303 patients with early-stage breast cancer were identified, 43% of whom received adjuvant chemotherapy. For the 9 QIs for which receiving the service or outcome was desirable (ie, consultation with a medical oncologist), the benchmark varied from 40.9% to 100%. For the 6 indicators for which not receiving the service or outcome was desirable (ie, incidence of febrile neutropenia), the benchmark varied from 0% to 49.0%. There was substantial variation noted with regard to the number of institutions meeting the target and the amount of interinstitution variation between the QIs. Top performing institutions varied by indicator, with no individual institution meeting the benchmark for all indicators. For the majority of indicators, institution size was not found to be correlated with performance. CONCLUSIONS: Data-derived benchmarking can be used to facilitate quality improvement by identifying areas of both good as well as suboptimal performance while defining an achievable target for which to strive. Cancer 2017;123:3772-3780. © 2017 American Cancer Society.
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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 | 0.085 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".