Assessing the quality of chemotherapy in an episode-based funding model in Ontario.
Bibliographic record
Abstract
1 Background: On April 1, 2014, the delivery of systemic treatment in Ontario transitioned to an episode-based funding model, which provides bundled payments for a full course of therapy delivered with adjuvant/curative intent and a monthly bundled payment for palliative intent. Disease Site Teams have defined treatment options and “best practice” for each funded adjuvant/curative regimen. Methods: The utilization of treatment according to disease and intent was analyzed for 35 systemic treatment facilities in Ontario, comparing actual practice (AP) with “best practice” (BP) (dose/schedule/cycle number), for common adjuvant/curative regimens. Variation in the choice of chemotherapy regimen was also reviewed. Results: Cases were included if they started a new course of adjuvant/curative therapy after January 1, 2014, and completed their treatment before July 31, 2015 (17,341 cases). For all disease sites and facilities, AP was equal to BP in 34.6% of cases, AP < BP in 57.5%, and AP > BP in 7.8% (Table). There was significant variation by disease site and facility. As a specific example, 1,368 courses of adjuvant mFOLFOX were completed for colorectal cancer: AP = BP (12 cycles) in 34% (range 7-70%), AP < BP in 66% (range 30-93%), and AP > BP in 1% (range 0-4%). Variation in choice of adjuvant regimen for breast cancer was also observed. Provincially, 21.5% of patients received non-anthracycline containing therapy, but the range in the 20 largest volume centers was 0% to 39.8%. Conclusions: With the administration of potentially curative therapy, it might be assumed that BP would be administered in the majority of cases. However, initial results demonstrate significant variation in practice in an episode based funding model. There is a need to understand the reasons for this variation and to identify potential opportunities to drive efficiency and to increase standardization by engaging clinical experts and practitioners. [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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".