Bibliographic record
Abstract
Use of the term 'carcinoid tumour' to describe a unique type of tumour in the gastroenteropancreatic system is endemic in the medical literature and in daily clinical and pathological parlance. However, it is a somewhat misleading moniker because a spectrum of histopathological changes and hence, biological outcomes may occur in these tumours. The World Health Organization classification scheme recommends the use of the terms neuroendocrine tumours or carcinomas, which may be stratified as well-differentiated neuroendocrine tumours with benign or uncertain behaviour, well-differentiated tumours with low-grade neuroendocrine carcinoma behaviour and high-grade neuroendocrine carcinomas. These categories may be applied within different sites in the gastrointestinal tract and pancreas, and convey a sense of biological behaviour. In addition, a recently suggested tumour-node-metastasis scheme has been proposed and awaits clinical validation and acceptance. Thus, the term 'carcinoid' has served its purpose well, but its use should be phased out in favour of 'neuroendocrine tumour' or 'neuroendocrine carcinoma'.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".