Fragmentation of Multimodal Rectal Cancer Care: A Population‐Level Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Locally advanced rectal cancer is often treated with multimodal therapy. Patients may receive care at a single institution or across multiple institutions. We designed this population-level retrospective cohort to determine the association between fragmented care and timeliness of treatment and long-term oncologic outcomes. METHODS: Patients with stage II/III rectal cancer who received at least two treatment modalities between 2010 and 2019 in Ontario, Canada were included. Fragmented care was defined as receiving at least one treatment modality at two or more institutions, while nonfragmented care was defined as receiving all treatments at a single institution. The primary outcome was timeliness of treatment as defined by Cancer Care Ontario Recommendations. Secondary outcomes included overall survival (OS). RESULTS: Overall, 3381 patients received fragmented care and 2026 patients received nonfragmented care. Patients receiving nonfragmented care were more likely to undergo timely initiation of treatment (OR: 1.72, 95% CI: 1.50-1.97, p < 0.0001). This was driven by timely initiation of chemotherapy (OR: 1.32, 95% CI: 1.16-1.49, p < 0.0001). There was little to no difference in OS (HR: 1.11, 95% CI: 0.95-1.30, p = 0.19). CONCLUSION: Patients with stage II/III rectal cancer receiving multimodal therapy may experience less timely initiation of treatment if their cancer care is fragmented. This did not translate into differences in long-term oncologic outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".