Variation in the use of adjuvant chemotherapy following neoadjuvant radiotherapy and surgery for rectal cancer in a publicly-insured health care system.
Bibliographic record
Abstract
854 Background: The efficacy of routine administration of adjuvant chemotherapy following sequential neoadjuvant chemoradiotherapy and surgery for rectal cancer is uncertain. This uncertainty may lead to practice pattern variations, with significant downstream discrepancies in oncological outcomes, patient-centered outcomes, and healthcare costs. The objective of this study, therefore, was to evaluate patient, disease, and health system factors associated with receipt of adjuvant chemotherapy following neoadjuvant radiotherapy and proctectomy. Methods: A retrospective cohort study of patients diagnosed with rectal cancer undergoing preoperative radiotherapy prior to proctectomy from January 1, 2010 to December 31, 2014 was performed using linked administrative healthcare databases. We compared the rate of chemotherapy administration (≥ 1 billing record) within 180 days of index rectal resection by healthcare administrative region in Ontario, Canada (2014 population: 13.4 million). Multivariable logistic regression models were constructed to assess patient, disease, and health system factors associated with differences in receipt of adjuvant chemotherapy. Results: We studied 1668 patients treated with preoperative radiotherapy and proctectomy, of whom 67% received chemotherapy within 180 days after surgery. The rate of adjuvant chemotherapy administration varied among health regions from 54% to 93%. On multivariable analysis, health region of residence, younger patient age, lower baseline comorbidity burden, and pathological nodal involvement were significant predictors of receipt of adjuvant chemotherapy. Conclusions: There is significant variation in receipt of adjuvant chemotherapy for patients receiving preoperative radiotherapy followed by proctectomy in Ontario. This variability is associated with patient, disease, and health system-related factors. Identifying the drivers of variability in cancer care practice may help to provide a basis for understanding and addressing discrepancies in clinical, patient-centered, and economic outcomes in healthcare systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".