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Record W1932946942 · doi:10.1186/1742-6413-2-12

Fine-needle aspiration of the thyroid: an overview.

2005· article· en· W1932946942 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCytoJournal · 2005
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
FundersUniversity of Pennsylvania
KeywordsMedicineThyroid nodulesThyroidDifferential diagnosisFine-needle aspirationCytologyFine needle aspiration cytologyRadiologyPathologyGeneral surgeryBiopsyInternal medicine

Abstract

fetched live from OpenAlex

Thyroid nodules (TN) are a common clinical problem. Fine needle aspiration (FNA) of the thyroid now is practiced worldwide and proves to be the most economical and reliable diagnostic procedure to identify TNs that need surgical excision and TNs that can be managed conservatively. The key for the success of thyroid FNA consists of an adequate or representative cell sample and the expertise in thyroid cytology. The FNA cytologic manifestations of TNs may be classified into seven working cytodiagnostic groups consisting of a few heterogenous lesions each to facilitate the differential diagnosis. Recent application of diagnostic molecular techniques to aspirated thyroid cells proved to be useful in separating benign from malignant TNs in several cases of indeterminate lesions.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.403
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

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

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 head0.051
GPT teacher head0.332
Teacher spread0.281 · 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