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Record W4323043949 · doi:10.3390/cancers15051575

Characteristics of PTEN Mutation in Thyroid Tumours: A Retrospective Chart Review

2023· article· en· W4323043949 on OpenAlexaff
Saruchi Bandargal, Mohannad Rajab, Véronique‐Isabelle Forest, Marc Pusztaszeri, Michael P. Hier, Sabrina Daniela da Silva, Richard J. Payne

Bibliographic record

VenueCancers · 2023
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsRoyal Victoria HospitalJewish General HospitalMcGill University
Fundersnot available
KeywordsPTENMedicineMalignancyThyroidRetrospective cohort studyThyroidectomyPathologyThyroid cancerThyroid carcinomaThyroid nodulesInternal medicineOncologyCancer researchGastroenterologyBiologyPI3K/AKT/mTOR pathwayGenetics

Abstract

fetched live from OpenAlex

While some studies suggest that PTEN mutations correlate with a low-risk phenotype in pediatric thyroid nodules, the relationship between the mutation and malignancy in the adult populations is abstruse. This study investigated whether PTEN mutations result in thyroid malignancy, and whether these malignancies are aggressive. This multicenter study involved 316 patients who underwent preoperative molecular testing, and subsequent lobectomy or total thyroidectomy at two quaternary care hospitals. A four-year retrospective review was performed on the 16 charts of patients that opted for surgery following a positive PTEN mutation on molecular testing results from January 2018 to December 2021. Of the total 16 patients, 37.5% (n = 6) had malignant tumours, 18.75% (n = 3) had non-invasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTPs), and 43.75% (n = 7) had benign disease. Aggressive features were detected in 33.33% of the malignant tumours. Malignant tumours were found to have a statistically significant higher allele frequency (AF). The aggressive nodules were all poorly differentiated thyroid carcinomas (PDTCs) with copy number alterations (CNAs) and the highest AFs.

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 score0.160
Threshold uncertainty score0.347

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 head0.019
GPT teacher head0.300
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

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

Citations22
Published2023
Admission routes1
Has abstractyes

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