Loco-regional outcomes of a population-based cohort of rectal neuroendocrine tumors.
Bibliographic record
Abstract
668 Background: Optimal management of rectal neuroendocrine tumors (NETs) is not well defined. We characterized the clinicopathologic features, loco-regional, and systemic management of a population-based cohort of rectal NETs. Methods: Patients diagnosed with rectal NETs from 1999-2011 were identified from British Columbia provincial databases. NETs were classified as G1 and G2 tumors with a Ki-67 ≤ 20% and/or mitotic count ≤ 20 per high power field. Results: Of 91 rectal NETs, median age was 58 (IQR 48-65) years and 35 (38%) were male. Median tumor size was 6 (IQR 4-8) mm. Median overall survival was 164.7 months, with 3 patients presenting with stage IV disease. Treatment included local excision (n = 79), surgical resection (n = 6), and pelvic radiation (n = 1; T3N1 tumor). Final margin status was positive in 17 (20%) cases. Local relapse occurred in 8 (9%) cases, and one relapse to bone 13 months after T3N1 tumor resection. Univariate analysis demonstrated an association between local relapse and T classification, Ki-67, mitotic count, grade, and perineural invasion (p< 0.01), but not N or M classification, or lymphovascular invasion. Local relapse was not associated with surgical management or margin status. Of 3 patients with metastatic disease, two received systemic management, with capecitabine and temozolomide. Conclusions: Rectal NETs generally presented with small, early tumors and were treated with local excision or surgical resection without pelvic radiation. [Table: see text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".