Carcinoids and Capsules: A Case Series Highlighting the Utility of Capsule Endoscopy in Patients With Small Bowel Carcinoids
Bibliographic record
Abstract
BACKGROUND: Neuroendocine tumors (NETs) or carcinoids arise at many different sites of the gastrointestinal tract. The small intestine is the most common site for NETs. Diagnosing small bowel carcinoids remains challenging given their non-specific presentations and the overall low incidence of small bowel tumors. Video capsule endoscopy (VCE) has significanly improved our ability to detect small bowel malignancies. We explore the value of VCE in the initial workup and management of a series of small bowel carcinoid patients. METHODS: We retrospectively analyzed adult patients undergoing surgical management for small bowel lesions from July 2005 to September 2015 at a tertiary care center. Patient characteristics, presenting symptomatology, diagnostic workup and surgical management were analyzed among patients with histologically confirmed small bowel carcinoid tumors. RESULTS: Our study identified 16 patients treated surgically for small bowel carcinoids. The majority of patients (87.5%) presented with either occult gastrointestinal bleeding or anemia. Most patients (87.5%) were initially evaluated with various endoscopic and imaging modalities before all ultimately undergoing surgery. Seventy-five percent of patients had a VCE, with 83.3% (10/12) having positive findings that correlated with intraoperative findings compared to 62.5% (5/8) with computed tomography scan, 21.4% (3/14) with colonoscopy, 44% (4/9) with deep enteroscopy, and 0% (0/9) with esophagogastroduodenoscopy (EGD). CONCLUSIONS: In the absence of any contraindications, VCE is an effective endoscopic modality in the diagnostic workup of small bowel NETs. Furthermore, positive VCE findings appear to highly correlate with surgical findings, thus suggesting a valuable role for VCE in the initial surgical assessment of patients with small bowel NETs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".