NANETS Consensus Guideline for the Diagnosis and Management of Neuroendocrine Tumors
Bibliographic record
Abstract
Neuroendocrine tumors (NETs) of the thorax, including bronchial and thymic neuroendocrine NETs, are often referred to as NETs of the foregut. The incidence and prevalence of NETs are increasing in the United States as demonstrated in the Surveillance, Epidemiology, and End Results from 1973 to 2004 (J Clin Oncol. 2008;26[18]:3063-3072). Although the majority of bronchial and thymic NETs are sporadic, approximately 5% to 10% can be associated with hereditary syndrome, multiple endocrine neoplasms type 1 (Nat Rev Cancer. 2005;5[5]:367-375). Diagnosis is made by tissue pathology, allowing for characterization and classification of the NET. Radiologic evaluation is performed to determine the extent of disease involvement. Clinical symptoms from hormonal overproduction or from paraneoplastic processes are medically managed to improve patients' quality of life. Locoregional disease can be curative with surgery; however, distant or metastatic disease is rarely curable. Therapeutic options for metastatic/advanced NETs of the thorax are mainly to palliate symptoms. Final treatment recommendations for patients with either bronchial or thymic NETs should be individualized, weighing the risks and benefits of therapy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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".