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Record W4297912724 · doi:10.1002/jbmr.4714

Etiology and Pathophysiology of Hypoparathyroidism: A Narrative Review

2020· review· en· W4297912724 on OpenAlexaff
Janice L. Pasieka, Kelly L. Wentworth, Caitlin T. Yeo, Serge Cremers, David W. Dempster, Seiji Fukumoto, Ravinder Goswami, Pascal Houillier, Michael A. Levine, Jesse D. Pasternak, Nancy D. Perrier, Antonio Sitges‐Serra, Dolores Shoback

Bibliographic record

VenueJournal of Bone and Mineral Research · 2020
Typereview
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsUniversity of TorontoUniversity Health NetworkUniversity of Calgary
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesTakeda Pharmaceuticals U.S.A.
KeywordsHypoparathyroidismEtiologyMedicineParathyroid hormoneNarrative reviewEndocrine systemIntensive care medicineParathyroid glandHormoneBioinformaticsSurgeryInternal medicineCalciumBiology

Abstract

fetched live from OpenAlex

The approach utilized a systematic review of the medical literature executed with specifically designed criteria that focused on the etiologies and pathogenesis of hypoparathyroidism. Enhanced attention by endocrine surgeons to new knowledge about parathyroid gland viability are reviewed along with the role of intraoperative parathyroid hormone (ioPTH) monitoring during and after neck surgery. Nonsurgical etiologies account for a significant proportion of cases of hypoparathyroidism (~25%), and among them, genetic etiologies are key. Given the pervasive nature of PTH deficiency across multiple organ systems, a detailed review of the skeletal, renal, neuromuscular, and ocular complications is provided. The burden of illness on affected patients and their caregivers contributes to reduced quality of life and social costs for this chronic endocrinopathy. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.137
GPT teacher head0.458
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations71
Published2020
Admission routes1
Has abstractyes

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