Bibliographic record
Abstract
神経内分泌腫瘍は内分泌細胞を発生母地として全身に発生するが，消化器領域では膵臓と消化管が好発部位であり，膵・消化管神経内分泌腫瘍（gastroenteropancreaticneuroendocrine neoplasms：GEP-NEN）と総称される。希少疾患であるが，近年の疫学研究から疾患頻度の明らかな増加が報告されている1 ）2 ）。高分化型の神経内分泌腫瘍（neuroendocrine tumor：NET）と低分化型の神経内分泌がん（neuroendocrine carcinoma：NEC）に大別され，基本となる病理分類の理解が必須である。また，分子標的薬やソマトスタチンアナログの登場，内視鏡治療や外科治療の進歩も相まって，GEP-NENの集学的治療が大きく進歩している。GEP-NEN診療において，これらの多様化した治療法を適切に選択するには，正確な診断が何より重要であり，ホルモン産生能の評価，画像診断と組織学的診断がその中心となる。本稿では，2019年に改訂された膵・消化管神経内分泌腫瘍（NEN）診療ガイドライン第２ 版（以下，GEP-NEN診療ガイドライン）や病理組織学的分類（WHO分類）も踏まえて，GEP-NEN診断の進歩や今後の展望について概説する。
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".