Are we choosing wisely? Operationalizing Choosing Wisely in the Ontario cancer system.
Bibliographic record
Abstract
221 Background: The Choosing Wisely (CW) campaign aims to initiate conversations about unnecessary treatments contributing to the rising cost of cancer care. We aim to develop a data linkage research platform to operationalize CW recommendations within administrative health care databases and the population-based performance of these recommendations. We initiated testing with the CW recommendiation against routine surveillance imaging in patients with aggressive histology lymphoma and pancreatic/gastric (P/G) cancer treated with curative intent. Methods: We used population-based administrative databases from Ontario, Canada to examine a cohort of adult patients with diffuse large B-cell lymphoma (DLBCL) (2004-2011) and P/G cancer post-surgical resection (2003-2013). For the DLBCL cohort, we defined an index date of 2-years after the last dose of R-CHOP as the time-frame beyond which surveillance CT imaging would be inappropriate. For the P/G cohort, the index date was 6 months post-resection. The primary outcome was cumulative incidence of CT scans within 3 years of the index date. To ensure that only surveillance scans were captured, we censored 6 months prior to development of recurrent disease, a new cancer diagnosis, or death. Results: The cohort consisted of 2,838 DLBCL and 2,930 P/G patients. The cumulative incidence of receiving CT imaging in the three years post index date was 55.6% (95% CI 53.7%-57.5%) among DLBCL and 82.8% (95% CI 81.3%-84.3%) among P/G patients. DLBCL patients ≥65 were more likely to receive imaging (p<0.01) as were those with more comorbidities (p<0.01). Younger patients with P/G were more likely to receive imaging (p<0.01) as were men (p<0.01). Income and rurality did not predict for increased imaging in either cohort. Surveillance CT imaging decreased over time among DLBCL patients (p<0.01), but increased among P/G cancer patients (p<0.001). Conclusions: During a time-frame in which surveillance imaging is deemed unnecessary by the CW campaign, the practice in Ontario remains excessive. This study represents a real-world demonstration that CW statements can be operationalized within population-based administrative databases and used as quality indicators in cancer care.
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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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".