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Record W3159188962 · doi:10.1186/s40463-021-00500-6

Molecular testing for cytologically suspicious and malignant (Bethesda V and VI) thyroid nodules to optimize the extent of surgical intervention: A retrospective chart review

2021· article· en· W3159188962 on OpenAlexaffabout
Jessica Hier, Galit Avior, Marc Pusztaszeri, Joshua R. Krasner, Noura Alyouha, Véronique‐Isabelle Forest, Michael P. Hier, Alex Mlynarek, Keith Richardson, Nader Sadeghi, Michael Tamilia, Richard J. Payne

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsMcGill University Health CentreJewish General HospitalMcGill University
Fundersnot available
KeywordsThyroid nodulesMedicineThyroidectomyMalignancyThyroidThyroid cancerGeneral surgeryRadiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Molecular testing has been used for cytologically indeterminate thyroid nodules (Bethesda III and IV), where the risk of malignancy is 10-40%. However, to date, the role of molecular testing in cytologically suspicious or positive for malignancy (Bethesda V and VI) thyroid nodules has been controversial. The aim of this study was to determine whether patients who had molecular testing in Bethesda V and VI thyroid nodules had the optimal extent of surgery performed more often than patients who did not have molecular testing performed. METHODS: A retrospective chart review of 122 cases was performed: 101 patients from the McGill University teaching hospitals and 21 patients from the Hillel Yaffe Medical center, Technion University. Patients included in the study were those with Bethesda V or VI thyroid nodules who underwent molecular testing (ThyGenext® or ThyroseqV3®) (McGill n = 72, Hillel Yaffe n = 14). Patients with Bethesda V or VI thyroid nodules who did not undergo molecular testing were used as controls (McGill n = 29, Hillel Yaffe n = 7). Each case was reviewed in order to determine whether the patient had optimal surgery. This was defined as total thyroidectomy in the presence of either a positive lymph node, extrathyroidal extension, or an aggressive pathological variant of papillary thyroid carcinoma (tall cell, hobnail, columnar cell, diffuse sclerosing, and solid/trabecular) documented on the final pathology report. In all other cases, a lobectomy/hemi/subtotal thyroidectomy was considered as optimal surgery. Chi-squared testing was performed to compare groups. RESULTS: When molecular testing was done, 91.86% (79/86) of surgeries in the molecular testing group were optimal, compared to 61.11% (22/36) in the control group. At McGill University teaching hospitals and at Hillel Yaffe, 91.67% (66/72) and 92.86% (13/14) of surgeries in the intervention group were considered as optimal, respectively. This compares to 58.62% (17/29) at McGill and 71.43% (5/7) at Hillel Yaffe when molecular testing was not performed (p = .001, p = .186). CONCLUSIONS: In this study, molecular testing in Bethesda V and VI thyroid tumors significantly improved the likelihood of optimal surgery. Therefore, molecular testing may have an important role in optimizing surgical procedures performed in the setting of Bethesda V and VI thyroid nodules. Prospective studies with larger sample sizes are required to further investigate this finding.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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 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

Citations32
Published2021
Admission routes2
Has abstractyes

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