MétaCan
Menu
Back to cohort
Record W2109991194 · doi:10.1586/17446651.2014.887435

Preoperative diagnosis of thyroid nodules using the Bethesda System for Reporting Thyroid Cytopathology: a comprehensive review and meta-analysis

2014· review· en· W2109991194 on OpenAlexaff
Brandon S. Sheffield, Hamid Masoudi, Blair Walker, Sam M. Wiseman

Bibliographic record

VenueExpert Review of Endocrinology & Metabolism · 2014
Typereview
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicineCytopathologyThyroid nodulesThyroidMedical diagnosisFine-needle aspirationThyroid cancerBethesda systemMalignancyBiopsyRadiologyGeneral surgeryPathologyCytologyInternal medicine

Abstract

fetched live from OpenAlex

Fine-needle aspiration biopsy (FNAB) is the test of choice for the evaluation of nodules, arriving at a cancer diagnosis, and guiding surgical management. This review and meta-analysis aims to objectively evaluate the Bethesda System for Reporting Thyroid Cytopathology (BSRTC) based upon literature reports of histopathological outcomes following cytopathological diagnoses. Thirteen studies were reviewed and the risk of malignancy (ROM) for each of the BSRTC diagnostic categories were calculated as: Non-diagnostic 11-26%, Benign 4-9%, AUS/FLUS 19-38%, FN/SFN 27-40%, SFM 50-79%, and Malignant 98-100%. In typical clinical utilization, the sensitivity and specificity of thyroid FNAB diagnosis using the BSRTC were 96% and 46%, respectively. The BSRTC represents an important advance in standardizing thyroid FNAB cytopathological reporting. Close attention should be paid to the observation that the AUS-FLUS and FN-SFN DCs have overlapping ROMs, and the potential clinical implications of this finding on patient management.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0280.009
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.426
Teacher spread0.300 · 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 teacher head, not a consensus.

Study designMeta-analysis
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

Citations57
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of Endocrinology & MetabolismSame topicThyroid Cancer Diagnosis and TreatmentFrench-language works237,207