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Record W4409313298 · doi:10.1096/fj.202403264r

Advances in Subacute Thyroiditis: Pathogenesis, Diagnosis, and Therapies

2025· review· en· W4409313298 on OpenAlexaff
Yu‐Chuan Li, Yue Hu, Yi Zhang, Kewen Cheng, C ZHANG, Junhui Wang

Bibliographic record

VenueThe FASEB Journal · 2025
Typereview
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
Fundersnot available
KeywordsPathogenesisMedicineSubacute thyroiditisCytokine stormThyroiditisDiseasePandemicImmunologyThyroidBioinformaticsCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)PathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Subacute thyroiditis (SAT) is an inflammatory thyroid disease that is often associated with viral infections. In particular, SARS-COV-2 and its vaccine were found to cause SAT during the recent COVID-19 pandemic. However, the pathogenesis, clinical features, and processes still need further profiling. Recently, there are new findings and understanding about the pathogenic mechanisms of SAT. Some HLA genes have been shown to increase the risk of SAT development, and inflammatory cytokine storms could promote the progression of SAT. Some new diagnostic criteria for SAT have been proposed to facilitate clinicians' diagnosis of SAT when facing atypical symptoms in a manner of rapidity and accuracy. Plus, new treatments for SAT with herbal medicines have been proposed recently as an addition to the conventional steroidal drugs and NSAIDs. This review will provide a summary of these recent progresses of SAT on pathogenesis, diagnosis, and therapies with emphasis on the role of a variety of virus pathogens, including the COVID-19 virus.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.324
Teacher spread0.301 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

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Same venueThe FASEB JournalSame topicThyroid Disorders and TreatmentsFrench-language works237,207