Guideline- Versus Non-Guideline-Based Neoadjuvant Management of Clinical T4 Rectal Cancer
Bibliographic record
Abstract
(1) Background: Practice guidelines recommend neoadjuvant treatment for clinical T4 rectal cancer. The primary objective of this retrospective study was to assess whether compliance with guidelines correlates with patient outcomes. Secondarily, we evaluated predictors of adherence to guidelines and mortality. (2) Methods: A total of 397 qualified rectal cancer (RC) patients from 2017 to 2020 at West China Hospital of Sichuan University were included. Patients were divided into two groups depending on adherence to neoadjuvant treatment guidelines. The main endpoints were overall survival (OS) and disease special survival (DSS). We analyzed factors associated with guideline adherence and mortality. (3) Results: Compliance with guidelines was only 39.55%. Patients' neoadjuvant therapy treated not according to the guidelines for clinical T4 RC was not associated with an overall survival (95.7% vs. 88.9%) and disease special survival (96.3% vs. 91.1%) benefit. Patients were more likely to get recommended therapy with positive patient compliance. Staging Ⅲ, medium/high differentiation and objective compliance were associated with increased risk of mortality. (4) Conclusions: Guideline adherence for clinical T4 RC in our system is low. Compliance with the relevant guidelines for neoadjuvant therapy seems not to lead to better overall survival for patients with clinical T4 RC.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".