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Record W2792017581 · doi:10.7759/cureus.2348

Increased Cancer Risk in Younger Patients with Thyroid Nodules Diagnosed as Atypia of Undetermined Significance

2018· article· en· W2792017581 on OpenAlexaff
Emilija Todorovic, Brandon S. Sheffield, Steve E. Kalloger, Blair Walker, Sam M. Wiseman

Bibliographic record

VenueCureus · 2018
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaSt. Paul's Hospital
Fundersnot available
KeywordsMedicineAtypiaThyroid cancerNodule (geology)ThyroidMalignancyThyroid nodulesHistopathologyCancerBiopsyCytopathologyRetrospective cohort studyGynecologyInternal medicineCytologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The objective of this study was to determine if patient age and/or gender significantly alter the risk of thyroid malignancy in the Bethesda System for Reporting Thyroid Cytopathology (BSRTC) diagnostic categories. METHODS: A retrospective review of 291 sequential patients that underwent thyroid nodule fine needle aspiration biopsy (FNAB) and subsequent surgery at a single center was carried out. Cases were grouped according to age (55 years and older versus younger than 55 years) and gender. The cancer risk was calculated for each BSRTC diagnostic group. A p-value <0.05 was not considered statistically significant. RESULTS: The study population was composed of 291 patients (227 females and 64 males). Histopathology diagnosed cancer in 113 cases (39%). The cancer risk was significantly increased in cases with a BSRTC diagnosis of atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) in patients younger than 55 years of age (36.8% vs 7.4%, p=0.0082). CONCLUSIONS: Though thyroid cancer was significantly more common in males (p=0.021), gender did not significantly influence specific BRSTC diagnostic category cancer risk estimation. A BSRTC AUS/FLUS diagnosis is associated with an increased cancer risk in younger patients.

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)0.000
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 score0.016
Threshold uncertainty score0.995

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 head0.009
GPT teacher head0.262
Teacher spread0.253 · 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.

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

Citations6
Published2018
Admission routes1
Has abstractyes

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