Deciphering the Dilemma: Choosing the Optimal Total Neoadjuvant Treatment Strategy for Locally Advanced Rectal Cancer
Bibliographic record
Abstract
Rectal cancer management has evolved significantly, particularly with neoadjuvant treatment strategies. This narrative review examines the development and effectiveness of these therapies for locally advanced rectal cancer (LARC), highlighting the historical quest that led to current neoadjuvant alternatives. Initially, trials showed the benefits of adding radiotherapy (RT) and chemotherapy (CT) to surgery, reducing local recurrence (LR). The addition of oxaliplatin to chemoradiotherapy (CRT) further improved outcomes. TNT integrates chemotherapy and radiotherapy preoperatively to enhance adherence, timing, and systemic control. Key trials, including PRODIGE 23, CAO/ARO/AIO 12, OPRA, RAPIDO, and STELLAR, are analyzed to compare short-course and long-course RT with systemic chemotherapy. The heterogeneity and difficulty in comparing TNT trials due to different designs and outcomes are acknowledged, along with their promising long-term results. On the other hand, it briefly discusses the potential for non-operative management (NOM) in select patients, a strategy gaining traction due to favorable outcomes in specific trials. As a conclusion, this review underscores the complexity of rectal cancer treatment, emphasizing individualized approaches considering patient preferences and healthcare resources. It also highlights the importance of interpreting impressive positive or negative results with caution due to the variability in study designs and patient populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".