Estimating the optimal rate of adjuvant chemotherapy utilization in stage III colon cancer.
Bibliographic record
Abstract
6591 Background: Identifying optimal chemotherapy utilization rates can drive improvements in quality of care. We report a benchmarking approach to estimate the optimal rate of adjuvant chemotherapy (ACT) for stage III colon cancer. Methods: The Ontario Cancer Registry was linked to electronic chemotherapy records to identify ACT utilization among a random 25% sample of patients with stage III colon cancer diagnosed during 2002-2008 in Ontario, Canada. We explored whether hospital factors (teaching status, regional cancer centre, medical oncologist on-site) were associated with ACT rates. The benchmark population included hospitals with the highest ACT rates that accounted for 10% of the patient population. Hospital ACT rates were adjusted for case mix in a multi-level model accounting for random variation at the hospital level. A Monte Carlo simulation was used to estimate the proportion of observed ACT rate variation that could be due to chance alone. Results: The study population included 2,801patients with stage III colon cancer; ACT was delivered to 66% (1861/2801) of patients. There was no difference in hospital ACT rate by teaching status (64% academic vs 67% non-academic, p = 0.107), comprehensive cancer centre status (65% cancer centre vs 67% non-cancer centre, p = 0.362), or having medical oncology on site (67% on site vs 66% not on site, p = 0.840). After excluding hospitals that had case volumes less than 10 (N = 150), unadjusted ACT rates varied across hospitals (range 44% to 91%, p = 0.017). The unadjusted benchmark ACT rate was 81% (95%CI 76%-86%); utilization rate in non-benchmark hospitals was 65% (95%CI 63%-66%). When using adjusted ACT rates in a multi-level model significant variation remained across hospitals (p < 0.001). The adjusted benchmark ACT rate was 74% (95%CI 63%-83%); non-benchmark hospital ACT rate was 65% (95%CI 53%-75%). The simulation analysis suggested that the non-random component of ACT rate variation across hospitals was 1.5%. Conclusions: There is significant variation in ACT rates across hospitals in routine practice. The estimated benchmark ACT rate is 74%. However, simulation analyses suggest that most of the variation in ACT utilization across hospitals may be due to chance alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".