Achieving a Cure Without Total Mesorectal Excision in Rectal Adenocarcinoma
Bibliographic record
Abstract
The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors’ suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology , to patients seen in their own clinical practice. Rectal cancer is a curable disease, yet curing the disease can be associated with lifelong morbidity because of the nature of the curative-intent treatment strategies. A major focus of modern prospective trials has been to maintain current cure rates, while minimizing lifelong lifestyle alterations and maximizing quality of life. Navigating the complex landscape of therapeutic options for rectal adenocarcinoma with a focus to accomplish this quality-of-life improvement is a critical focus area for future clinical trials. Many challenges remain on the path to optimizing cure and minimizing morbidity, and include improving initial staging accuracy, more precise selection of neoadjuvant therapy used for each patient, choosing the optimal surgical management strategy, and ensuring modern radiation therapy approaches are being used. Finally, organ preservation strategies have moved to the forefront in the management of both early and locally advanced rectal cancers and hold the potential for significant changes to come for patients with rectal cancer. Herein, we highlight some of the challenges remaining in the field, progress made, and how the recent data from the Canadian Cancer Trials Group phase II trial can be put into context with the ACOSOG Z6041, CARTS, and GRECCAR2 trials.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".