Bibliographic record
Abstract
Carcinoid tumours are relatively rare and, in general, slow growing. They can be “non-functioning” tumours, presenting as a tumour mass, or “function-ing ” tumours secondary to the production of several biopeptides leading to the carcinoid syndrome. Though these tumours represent 0.25 % of an oncol-ogy practice, a proper understanding of the clinical course of the disease and of the importance of appro-priate diagnostic and therapeutic measures is very important. Proper patient management can lead to cure, particularly if the tumour can be fully resected, or to long-term palliation with medical treatment or cytoreductive surgery, or both, with significant pro-longation of survival. A good understanding of the use of somatostatin analogues to achieve effective symptomatic control and of the importance of ad-equate follow-up and cardiac monitoring to prevent or effectively treat cardiac complications can con-tribute significantly to optimal control of this com-plex disease, ultimately improving the quality of life of affected patients. This article, developed by a group of Canadian experts, provides a framework that will assist clinicians in taking an optimal approach to managing their patients with carcinoid tumour. KEY WORDS Carcinoid tumour, carcinoid syndrome, guidelines, clinical management 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.031 |
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".