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Record W2964756509 · doi:10.5430/jst.v9n2p32

Cytologic, histologic and molecular findings of papillary thyroid carcinoma variants, one institution’s experience

2019· article· en· W2964756509 on OpenAlexvenueno aff
Nadja Falk, Swarnamala Ratnayaka, Andrew B. Sholl, Krzysztof Moroz, Tatyana Kalinicheva

Bibliographic record

VenueJournal of Solid Tumors · 2019
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroblastoma RAS viral oncogene homologThyroid carcinomaCytologyMedicineThyroid cancerThyroidPathologyMolecular pathologyCarcinomaMutationCancerOncologyCancer researchInternal medicineBiologyGeneGeneticsKRAS

Abstract

fetched live from OpenAlex

Papillary thyroid carcinoma (PTC) has two major types, classic (PTCC) and follicular variant (FVPTC), which correlate with molecular findings and have varying clinical implications. We assessed the cytologic findings and subsequent surgical pathology findings with the molecular mutations in these two groups, including microcarcinomas. Fourty-four patients with PTC resections over a one-year period were retrospectively examined in conjunction with previous cytologic diagnoses. BRAF, NRAS and TERT promoter mutations for the resected specimens were analyzed. Correlation with previous cytology in regard to molecular mutations and tumor size (microcarcinoma) were made. Significantly more BRAF V600E mutations were seen with PTCC, whereas significantly more NRAS mutations were seen with FVPTC. TERT mutations were only seen with PTCC. Molecular studies for thyroid carcninomas are becoming increasingly more common and influence treatment and patient prognosis. BRAF and or TERT mutations are associated with a worse prognosis. NRAS mutations associated with FVPTC and may lead to milder cytologic changes compared to the BRAF- and TERT-driven PTCC.

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 score0.047
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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