Nowotwory neuroendokrynne jelita grubego — zasady postępowania (rekomendowane przez Polską Sieć Guzów Neuroendokrynnych)
Bibliographic record
Abstract
Neuroendocrine neoplasms/tumours (NENs/NETs) of the large intestine are detected increasingly often, especially rectal tumours, which is probably associated with the widespread use of screening colonoscopy. There is a growing body of evidence supporting the thesis that the NENs of the rectum and the NENs of the colon are two different diseases. Rectal NENs are usually small lesions, of low to moderate histological malignancy, associated with good prognosis, and most may be treated endoscopically. NENs of the colon, however, are often aggressive, poorly differentiated, associated with a poor or uncer-tain prognosis, and require surgical treatment. The management guidelines regarding these groups of patients are constantly changing. On the basis of the recent literature data and conclusions reached by the working meeting of the Polish Network of Neuroendocrine Tumours (December 2016), this study completes and updates the data and management guidelines regarding colorectal NENs published in Endokrynologia Polska 2013; 64: 358-368.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".