Neuroendocrine tumors (NET) of the gastrointestinal tract: A decade of experience at the Princess Margaret Hospital
Bibliographic record
Abstract
4093 Background: NET are uncommon malignancies with variable natural history and often indolent biological behavior. Over the past decade novel treatment approaches have been developed. There have been few recent descriptions of large institutional experiences with multi-modality therapy for this disease. Methods: The Princess Margaret Hospital cancer registry identified 207 patients (pts) with NET of the gastrointestinal tract or unknown primary who had their initial visits between 1992 and 2002. A retrospective chart review was performed. Results: The median follow-up duration was 25 months for pts who have died and 54 months for those alive at last follow-up. Demographics of the entire cohort of 207 pts were as follows: M:F = 53%:47%; mean age = 55 years; age-adjusted Charlson score 0 = 71%. Anatomical distribution of the primary location revealed that 33% were from the foregut, 37% from midgut, 13% from hindgut and 17% from unknown origin. Pathological distribution demonstrated that 73% were NET/carcinoids, 21% were islet cell tumors and 6% were small cell carcinomas. One-third of the cohort had biochemically and/or clinically functional tumors, 24% were non-functional, and 43% were not biochemically tested and were clinically non-functional. At presentation, 61% of pts had distant metastases and 39% had locoregional disease. Curative surgery was performed in 38% of the cohort, whereas radiotherapy and chemotherapy of any intent were delivered to 15% and 34% respectively. Octreotide LAR was part of the therapeutic regime in 13% of pts. Two-year survival analysis was evaluated among 114 pts, with the remaining cohort being excluded due to either insufficient follow-up or because pts were seen for second-opinion only. Factors that were significantly related to decreased survival in univariate analyses were the presence of distant metastases and small cell histology. All pts who underwent curative surgery in this subgroup were alive at 2 years, and 88% were alive at 5 years. Conclusions: Pts with NET who are offered curative surgery have a good outcome. Distant metastases are commonly found at presentation and portend a worse prognosis. These patients should be evaluated by a multidisciplinary team regarding new approaches to surgery and other therapies. No significant financial relationships to disclose.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".