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Record W4405802406 · doi:10.1001/jamaoto.2024.4453

Intraoperative Parathyroid Hormone Monitoring Criteria in Primary Hyperparathyroidism

2024· letter· en· W4405802406 on OpenAlexaff
Phillip Staibano, Michael Au, Han Zhang, Sheila Yu, Winnie Liu, Jesse D. Pasternak, Xing Xing, Carolyn D. Seib, Lisa A. Orloff, Nhu-Tram Nguyen, Michael K. Gupta, Eric Monteiro, Sameer Parpia, Tyler McKechnie, Alex Thabane, J. E. M. Young, Mohit Bhandari

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2024
Typeletter
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsMount Sinai HospitalUniversity of TorontoMcMaster UniversityUniversity Health NetworkImpact
Fundersnot available
KeywordsPrimary hyperparathyroidismMedicineParathyroidectomyParathyroid hormoneMeta-analysisCINAHLMEDLINEInternal medicine

Abstract

fetched live from OpenAlex

Importance: Intraoperative parathyroid hormone (IOPTH) monitoring is recommended by the American Association of Endocrine Surgeons for use during parathyroidectomy for patients with primary hyperparathyroidism (PHPT), but there is no clinician consensus regarding the IOPTH monitoring criteria that optimize diagnostic accuracy. Objective: To evaluate and rank the diagnostic properties of IOPTH monitoring criteria used during surgery for patients with PHPT. Data Sources: A bayesian diagnostic test accuracy network meta-analysis (DTA-NMA) was performed, in which peer-reviewed citations from January 1, 1990, to July 22, 2023, were searched for in MEDLINE, Embase, Web of Science, CENTRAL, and CINAHL. Study Selection: All full-text study designs that evaluated any IOPTH monitoring criteria as a diagnostic test were included in this meta-analysis. Any studies evaluating adult patients diagnosed with PHPT undergoing parathyroidectomy were also included. The reference standard used in this study was normalization of calcium and/or parathyroid hormone levels within 1 year of surgery. Data Extraction and Synthesis: This DTA-NMA was reported in accordance with the applicable Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. Two reviewers evaluated all abstracts and full-text articles using a piloted extraction form. A third author resolved any conflicts. There are no published Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) resources for DTA-NMA. The following conventional monitoring criteria were included: Halle, Miami, Rome, Vienna, and PTH normalization, and the following modified criteria were included: Miami and PTH normalization, modified Miami, and modified Vienna. A bayesian hierarchical DTA-NMA model with corresponding 95% credible intervals (CrIs) was used to describe the pooled diagnostic characteristics of the evaluated IOPTH monitoring criteria. Main Outcomes and Measures: Main outcomes included pooled diagnostic test properties, including sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio. Results: A total of 72 studies, which included 19 072 patients, met the inclusion criteria. Sixty-nine studies (95.8%) investigated classic PHPT. In PHPT, the Miami criteria were investigated most often and had the best diagnostic properties (diagnostic odds ratio, 60.00 [95% CrI, 32.00-145.00]) when compared to other conventional criteria. Moreover, the modified Miami criteria, which measures a postexcision IOPTH level 15 minutes or more postexcision of all hyperfunctioning parathyroid tissue, were the overall best criteria (diagnostic odds ratio, 79.71 [95% CrI, 22.46-816.67]). There was a low risk of study bias and no publication bias. Conclusions and Relevance: The results of this meta-analysis suggest that surgeons should use the modified Miami criteria when performing IOPTH-guided surgery for patients with PHPT because these criteria optimize intraoperative diagnostic accuracy by minimizing unnecessary neck exploration and revision surgery rates.

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)0.043
metaresearch head score (Gemma)0.163
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: Commentary · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.163
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.010
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.295
Teacher spread0.266 · 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
GenreCommentary

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

Citations19
Published2024
Admission routes1
Has abstractyes

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