Rectal Cancer in 2018: A Primer for the Gastroenterologist
Bibliographic record
Abstract
The rectum has distinctive anatomic and physiologic features, which increase the risk of local spread and recurrence among rectal cancers as compared to colon cancers. Essential to the management of rectal cancers is accurate endoscopic localization as well as preoperative imaging assessment of local and distant disease. Successful oncologic care is multidisciplinary including input from Gastroenterologists, Surgeons, Medical and Radiation Oncologists, Radiologists, and Pathologists. Extensive planning of curative intent is mandatory as failures of upfront treatment present great long-term difficulty for patients and caregivers. Local recurrences are frequently associated with major morbidity including bowel and urinary obstruction, severe pain, and significantly diminished quality of life. Distant recurrence is associated with lower survival. Over the last two decades, there have been many advances in diagnostic imaging techniques as well as surgical techniques including transanal endoscopic microsurgery for very early stage cancers. Progress in curative management paradigms includes shorter courses of preoperative radiotherapy and chemotherapy doublet paradigms for perioperative treatment. This review describes the diagnosis, workup, and multimodality curative intent treatment of rectal cancers. It is emphasized that success begins in the hands and eyes of the gastroenterologist.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".