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Record W4402064050 · doi:10.3803/enm.2024.2074

Pituitary Neuroendocrine Tumors in Multiple Endocrine Neoplasia

2024· review· en· W4402064050 on OpenAlexaff
Sang Ouk Chin, Constance L. Chik, Toru Tateno

Bibliographic record

VenueEndocrinology and Metabolism · 2024
Typereview
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMEN1Multiple endocrine neoplasiaMedicineEndocrine systemNeuroendocrine tumorsPituitary tumorsInternal medicinePathologyOncologyBiologyGeneticsHormoneGene

Abstract

fetched live from OpenAlex

Multiple endocrine neoplasia type 1 (MEN1) is an autosomal-dominant disorder characterized by tumors of the pituitary, parathyroid, and endocrine-gastrointestinal tract. Pituitary neuroendocrine tumors (PitNETs) occur in about 40% of MEN1 cases, with 10% being the first manifestation. Recent studies show a slight female predominance, with microPitNETs (<1 cm) being more common than macroPitNETs (>1 cm). Functional PitNETs (FPitNETs) are more frequent than non-functional ones (36% to 48%), with prolactinomas being the most common FPitNETs. MEN1-associated PitNETs are often plurihormonal, larger, and more invasive compared to sporadic types, though patient age and FPitNET proportions are similar. MEN1 mutation-negative patients tend to have larger, symptomatic PitNETs at diagnosis. Six patients with MEN1 have been reported to have pituitary carcinomas, including a mutation- negative patient. Treatment approach between PitNETs in MEN1 and sporadic types appears to be similar. PitNETs also occur in MEN4, but their epidemiology is less understood. In patients with a MEN1-like phenotype and negative genetic testing, MEN4 should be considered.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.047
GPT teacher head0.357
Teacher spread0.311 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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