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Record W2323950057 · doi:10.1097/pcr.0000000000000087

Granulomatous Thyroiditis Diagnosed by Fine-Needle Aspiration Cytology—A Case Report With Review of Other Benign Thyroid Entities

2015· article· en· W2323950057 on OpenAlexaff
Yonca Kanber, Manon Auger

Bibliographic record

VenuePathology Case Reviews · 2015
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineThyroidNodule (geology)ThyroiditisThyroid nodulesFine needle aspiration cytologyFine-needle aspirationPathologySubacute thyroiditisRadiologyCytologyBiopsyInternal medicine

Abstract

fetched live from OpenAlex

Fine-needle aspiration cytology is important in the evaluation of thyroid nodules; its results guiding management and often reducing unnecessary surgery to the patients harboring benign nodules. Most thyroid nodules are benign, and the most commonly aspirated lesion is the benign follicular nodule. Other benign conditions include inflammatory and autoimmune disorders. This review discusses the clinical and cytological findings of various benign entities involving the thyroid gland, including benign follicular nodule, Graves disease, lymphocytic (Hashimoto) thyroiditis, suppurative thyroiditis, Riedel thyroiditis, and granulomatous (subacute, de Quervain) thyroiditis; a case report of the latter is presented.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.304
Teacher spread0.269 · 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 designCase report
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

Citations0
Published2015
Admission routes1
Has abstractyes

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