The Use of MR Imaging in Treatment Planning for Patients with Rectal Carcinoma: Have You Checked the “DISTANCE”?
Bibliographic record
Abstract
Rectal cancer is a common and serious disease in the Western hemisphere. Optimal treatment of rectal cancer involves a multidisciplinary approach, with collaboration required between radiologists, oncologists, surgeons, and pathologists to achieve local control and decrease the rate of recurrence. Several studies have been published that show the ability to accurately stage rectal cancer with magnetic resonance (MR) imaging. Moreover, advances in preoperative therapies require accurate preoperative staging with MR imaging to select those patients who may benefit from more intensive treatment, without subjecting those who will not benefit to unnecessary treatment. As we enter an era of individualized patient care, stratified according to the risk of both local and distant failure, imaging takes on the same importance as the tumor type and genetic susceptibility. MR imaging is now an essential tool to enable the oncology team to make appropriate treatment decisions. However, rectal cancer evaluation with MR imaging remains a challenge in the hands of nonexperts. This article describes a mnemonic device, "DISTANCE," to enable a systematic approach to the interpretation of MR images, thereby enabling all the clinically relevant features to be adequately assessed: DIS, for Distance from the Inferior part of the tumor to the transitional Skin; T, for T staging; A, for Anal complex; N, for Nodal staging; C, for Circumferential resection margin; and E, for Extramural vascular invasion.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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".