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Record W4309737381 · doi:10.3390/metabo12111150

Exploring the Relationship between Obesity, Metabolic Syndrome and Neuroendocrine Neoplasms

2022· review· en· W4309737381 on OpenAlexaff
Xiaoyang Lan, Nicola Fazio, Omar Abdel‐Rahman

Bibliographic record

VenueMetabolites · 2022
Typereview
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsObesityMedicineIncidence (geometry)CancerNeuroendocrine tumorsMetabolic syndromeBioinformaticsIntensive care medicineInternal medicineOncologyBiology

Abstract

fetched live from OpenAlex

Obesity is a major burden for modern medicine, with many links to negative health outcomes, including the increased incidence of certain cancer types. Interestingly, some studies have supported the concept of an "Obesity Paradox", where some cancer patients living with obesity have been shown to have a better prognosis than non-obese patients. Neuroendocrine neoplasms (NENs) are malignancies originating from neuroendocrine cells, in some cases retaining important functional properties with consequences for metabolism and nutritional status. In this review, we summarize the existing evidence demonstrating that obesity is both a risk factor for developing NENs as well as a good prognostic factor. We further identify the limitations of existing studies and further avenues of research that will be necessary to optimize the metabolic and nutritional status of patients living with NENs to ensure improved outcomes.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.272
GPT teacher head0.388
Teacher spread0.116 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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