Carcinoid syndrome symposium on treatment modalities for gastrointestinal carcinoid tumours: symposium summary.
Bibliographic record
Abstract
OBJECTIVE: To develop a collaborative approach for the treatment of gastrointestinal carcinoid tumours and carcinoid syndrome. PARTICIPANTS: Leaders in the medical, endocrine, radiologic and surgical treatment of carcinoid disease were selected to present papers at the Carcinoid Syndrome Symposium on Treatment Modalities for Gastrointestinal Carcinoid Tumours and participate in the workshop that followed. EVIDENCE: A multidisciplinary symposium with experts in the field of carcinoid syndrome was organized at the University of Calgary. Data presented, participation of the attendees and a review of the literature were used in the workshop to develop a collaborative approach to the management of carcinoid tumours. BENEFITS: Carcinoid tumours are rare and few centres have large experiences in their treatment. Before the development of this collaboration, patients with carcinoid tumours received a unidisciplinary approach depending on referral patterns. The development of a multidisciplinary neuroendocrine clinic helped to unify the approach to these patients, yet a consensus on the treatment of carcinoid tumours was lacking. The expertise at the symposium allowed for consensus and the development of treatment algorithms, including biochemical screening, radiographic localization and surgical intervention, for gastrointestinal carcinoid tumours. The role of medical and hormonal therapy after cytoreducion is presented. RECOMMENDATION: Patients with carcinoid tumours require a multidisciplinary approach to their care.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".