Adoption of Total Neoadjuvant Therapy in the Treatment of Locally Advanced Rectal Cancer
Bibliographic record
Abstract
Local and metastatic recurrence are primary concerns following the treatment of locally advanced rectal cancer (LARC). Chemoradiation (CRT) can reduce the local recurrence rates and has subsequently moved to the neoadjuvant setting from the adjuvant setting. Pathological complete response (pCR) rates have also been noted to be greater in patients treated with neoadjuvant CRT prior to surgery. The standard approach to treating LARC would often involve CRT followed by surgery and optional adjuvant chemotherapy and remained the treatment paradigm for almost two decades. However, patients were often unable to complete adjuvant chemotherapy due to a decreased tolerance of chemotherapy following surgery, which led to upfront treatment with both CRT and chemotherapy, and total neoadjuvant therapy, or TNT, was created. The efficacy outcomes of local recurrence, disease-free survival, and pCR have improved in patients receiving TNT compared to the standard approach. Additionally, more recent data suggest a possible improvement in overall survival as well. Patients with a complete clinical response following TNT have the opportunity for watch-and-wait surveillance, allowing some patients to undergo organ preservation. Here, we discuss the clinical trials and studies that led to the adoption of TNT as the standard of care for LARC, with the possibility of watch-and-wait surveillance for patients achieving complete responses. We also review the possibility of overtreating some patients with LARC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".