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Record W4293421387 · doi:10.1002/jbmr.4682

Management of Primary Hyperparathyroidism

2020· article· en· W4293421387 on OpenAlexaff
John P. Bilezikian, Shonni J. Silverberg, Francisco Bandeira, Filomena Cetani, Manju Chandran, Natalie E. Cusano, Peter R. Ebeling, Anna Maria Formenti, Morten Frost, Jessica E. Gosnell, E. Michael Lewiecki, Frederick R. Singer, Neil Gittoes, Aliya Khan, Claudio Marcocci, Lars Rejnmark, Zhikang Ye, Gordon Guyatt, John T. Potts

Bibliographic record

VenueJournal of Bone and Mineral Research · 2020
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsImpactMcMaster University
FundersTakeda Pharmaceutical Company
KeywordsPrimary hyperparathyroidismMedicineHyperparathyroidismInternal medicine

Abstract

fetched live from OpenAlex

Since the last international guidelines were published in 2014 on the evaluation and management of primary hyperparathyroidism (PHPT), new information has become available with regard to evaluation, diagnosis, epidemiology, genetics, classical and nonclassical manifestations, surgical and nonsurgical approaches, and natural history. To provide the most current summary of these developments, an international group, consisting of over 50 experts in these various aspects of PHPT, was convened. This paper provides the results of the task force that was assigned to review the information on the management of PHPT. For this task force on the management of PHPT, two questions were the subject of systematic reviews using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) methodology. The full report addressing surgical and nonsurgical management of PHPT, utilizing the GRADE methodology, is published separately in this series. In this report, we summarize the results of that methodological review and expand them to encompass a much larger body of new knowledge that did not specifically fit the criteria of the GRADE methodology. Together, both the systematic and narrative reviews of the literature, summarized in this paper, give the most complete information available to date. A panel of experts then considered the last set of international guidelines in light of the newer data and assessed the need for their revision. This report provides the evidentiary background to the guidelines report. In that report, evidence from all task forces is synthesized into a summary statement and revised guidelines for the evaluation and management of PHPT. © 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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.103
GPT teacher head0.377
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations79
Published2020
Admission routes1
Has abstractyes

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