High-Dose-Rate Preoperative Endorectal Brachytherapy for Patients with Rectal Cancer
Bibliographic record
Abstract
High-dose-rate endorectal brachytherapy (HDREBT) is a novel treatment modality for patients with rectal cancer. It is based on modern tumor imaging, in particular magnetic resonance imaging (MRI), which is used to choose eligible patients and improve tumor visualization. Treatment planning is performed using 3D CT simulation and 3D treatment planning. Treatments are delivered on an outpatient basis and require minimal local anesthesia. Validation of this technique was carried out through a preoperative study. In this chapter, we describe the technical aspects of HDREBT and the ongoing studies exploring the clinical applications of this treatment modality for patients with rectal cancer: As neoadjuvant treatment for patients with operable rectal tumor As a dose escalation modality following external beam radiation for elderly and medically inoperable patients As a modality to promote to sphincter preservation surgery (SPS) for patients with low-lying rectal cancer As an option to improve local control in patients with newly diagnosed rectal cancer in the setting of previous pelvic radiation therapy These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".