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Record W2626394708 · doi:10.3389/fonc.2017.00119

Low Grade Neuroendocrine Tumors of the Lung

2017· review· en· W2626394708 on OpenAlexaff
Barbara Melosky

Bibliographic record

VenueFrontiers in Oncology · 2017
Typereview
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsNeuroendocrine tumorsMedicineLungContext (archaeology)SomatostatinSomatostatin receptorOctreotideCarcinoid tumorsPeptide receptorInternal medicineOncologyPathologyReceptorBiology

Abstract

fetched live from OpenAlex

The lung is the second most common site of neuroendocrine tumors (NETs). Typical and atypical carcinoids are low-grade NETs of the lung. They present a favorable prognosis comported to the more common high-grade NETs. The low- and high-grade NETs require different treatment strategies; effective management of these tumors is essential to prolong survival and to manage the symptoms in patients with secretory or functional tumors. These rare tumors have received little attention and education is needed for treating physicians. This mini-review will concentrate mainly on advanced low-grade lung NETs. The article describes the classification of lung NETs and the diagnostic work-up. Different treatment methods including somatostatin analogs, peptide receptor radioligand therapy, and biologic systemic therapy are discussed. Promising results from recent trials are presented and discussed in the context of the lung primary site.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.428
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2017
Admission routes1
Has abstractyes

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