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Record W3105920224 · doi:10.1001/jamaoto.2020.4021

Use of Intraoperative Parathyroid Hormone in Minimally Invasive Parathyroidectomy for Primary Hyperparathyroidism

2020· review· en· W3105920224 on OpenAlexaboutno aff
Alanna Jane Quinn, Éanna J. Ryan, Stephen Garry, Danielle L. James, Michael R. Boland, Orla Young, Michael J. Kerin, Aoïfe Lowery

Bibliographic record

VenueJAMA Otolaryngology–Head & Neck Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePrimary hyperparathyroidismParathyroidectomyOdds ratioObservational studyHazard ratioEndocrine surgeryConfidence intervalMeta-analysisSurgeryParathyroid hormoneGeneral surgeryInternal medicineThyroid

Abstract

fetched live from OpenAlex

Importance: Intraoperative parathyroid hormone (ioPTH) is a surgical adjunct that has been increasingly used during minimally invasive parathyroidectomy (MIP). Despite its growing popularity, to our knowledge a meta-analysis comparing MIP with ioPTH vs MIP without ioPTH has not yet been conducted. Objective: To evaluate the safety and efficacy of MIP with ioPTH for treatment of primary hyperparathyroidism. Data Sources: A systematic search of the databases PubMed, Embase, Scopus, Web of Science, and Cochrane Collaboration was performed to identify studies that compared MIP with and without ioPTH. Data were analyzed between August and September 2019. Study Selection: Inclusion criteria consisted of randomized clinical trials and observational studies with a retrospective/prospective design, comparing MIP using ioPTH vs MIP not using ioPTH for treatment of primary hyperparathyroidism. Eligible studies had to present odds ratio (OR), risk ratio, or hazard ratio estimates (with 95% CI), standard errors, or number of events necessary to calculate these for the outcome of interest rate. Studies involving patients with secondary or tertiary hyperparathyroidism or those with multiple endocrine neoplasia syndrome were excluded. Data Extraction: Two reviewers independently reviewed the literature according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. Dichotomous variables were pooled as ORs while continuous variables were compared using weighted mean differences. Quality assessment was performed using the Newcastle-Ottawa Scale. Main Outcomes and Measures: The primary outcome was rate of cure. Secondary outcomes included need for reoperation, need for bilateral neck exploration, morbidity, and length of surgery. Results: A total of 12 studies, involving 2290 patients with primary hyperparathyroidism, were eligible for inclusion. The median (SD) age of participants was 60.1 (11.8) years and 77.3% of participants were women. The median Newcastle-Ottawa score was 7. Patients who underwent MIP with ioPTH had higher cure rates (OR, 3.88; 95% CI, 2.12-7.10; P < .001). There was a greater need for reoperation in the group of patients who had surgery without ioPTH (OR, 0.40; 95% CI, 0.19-0.86; P = .02). There was a trend toward longer operating times/increased duration of surgery in the ioPTH group; however, this did not reach statistical significance (weighted mean difference, 21.62 minutes; 95% CI, -0.93 to 44.17 minutes; P = .06). The use of ioPTH was associated with higher rates of bilateral neck exploration (OR, 3.55; 95% CI, 1.27-9.92; P = .02). Conclusions and Relevance: Use of ioPTH is associated with higher cure rates for patients with primary hyperparathyroidism undergoing MIP. Minimally invasive parathyroidectomy performed without ioPTH is associated with less conversion to bilateral neck exploration at initial surgery but with lower cure rates and an increased risk for reoperation. Trial Registration: PROSPERO identifier: CRD42020148588.

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)0.016
metaresearch head score (Gemma)0.054
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 score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.074
GPT teacher head0.324
Teacher spread0.250 · 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".

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Citations53
Published2020
Admission routes1
Has abstractyes

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