Impact of grade on workup of rectal neuroendocrine tumors: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Rectal neuroendocrine tumors (RNETs) are often discovered on screening colonoscopy. Indications for staging and definitive resection are inconsistent in current guidelines. We evaluated the role of grade in guiding staging and procedural decision-making. METHODS: Patients with biopsy confirmed RNETs between 2004 and 2015 were reviewed. Baseline characteristics, staging investigations (biochemical and imaging), and endoscopic/surgical treatment were recorded. Associations between grade, preoperative staging, interventions, and survival were determined using Fisher-Freeman-Halton Exact, log-rank, and Kaplan-Meier analysis. RESULTS: Amongst 139 patients with RNETs, 9% were aged ≥ 75 years and 44% female. Tumor grade was: 73% grade 1 (G1), 18%, grade 2 (G2) and 9% grade 3 (G3). Staging investigations were performed in 52% of patients. All serum chromogranin A and 97% of 24-hour urine 5-hydroxyindoleacetic acid tests were normal. The large majority of staging computed tomography (CT) scans were negative (76%) with subgroup analysis showing no G1 patients with CT identified distant disease compared with 38% of G2 and 50% of G3 patients (p < 0.001). G1 patients were more likely to achieve R0/R1 resections compared to G2 (95% vs. 50%, p < 0.001) and G1 patients had significantly better 5-year overall survival (G1: 98%, G2: 67%, G3: 10%, p < 0.001). CONCLUSION: Tumor grade is important in preoperative workup and surgical decision-making. Biochemical staging may be omitted but staging CT should be considered for patients with grade ≥ 2 lesions. Anatomic resections should be considered for patients with grade 2 disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".