Neuroendocrine Tumors of the Gastrointestinal Tract
Bibliographic record
Abstract
OBJECTIVES: Neuroendocrine tumors (NETs) are uncommon malignancies with variable natural history and often indolent biological behavior. Over the past decade, novel treatment approaches have been developed. The purpose of this study was to review the experience at the Princess Margaret Hospital in treating patients with NET over the past decade. METHODS: The Princess Margaret Hospital Cancer Registry identified 193 patients 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 22.6 months. Demographics of the entire cohort of 193 patients were: male, 53%; mean age, 56 years; age-adjusted Charlson score 0, 71%. Anatomic distribution of the primary location revealed that 35% were from the foregut, 40% from midgut, 11% from hindgut, and 14% from unknown origin. Pathological distribution demonstrated that 72% were NET/carcinoids, 21% were islet cell tumors, and 6% were small cell carcinomas. At presentation, 53% of patients had distant metastases and 46% had loco-regional disease. Curative surgery was performed in 35% of the cohort, whereas radiotherapy and chemotherapy of any intent were delivered to 13% and 34%, respectively. For the entire cohort, 5-year survival was 58% (95% confidence interval, 50%-67%). In multivariate analysis, age, location of primary, and curative intent of surgery were independent predictors of survival. Of patients who underwent curative surgery, 86% were alive at 5 years. CONCLUSION: Patients 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.
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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 | 0.000 | 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) | 0.001 | 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".