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Record W4399694665 · doi:10.1210/clinem/dgae354

Seven years of Non-invasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features (NIFTP): Rate of Acceptance and Variation of Diagnostic Approaches Across Different Continents

2024· article· en· W4399694665 on OpenAlexaff
Michelle D. Williams, Zhiyan Liu, Esther Diana Rossi, Shipra Agarwal, Aleš Ryška, Abir Al Ghuzlan, Andrey Bychkov, Zubair Baloch, Rebecca D. Chernock, Simion L Chiosea, Nicole A. Cipriani, Suna Erkılıç, M. Fridman, Jen‐Fan Hang, Agnes Stephanie Harahap, Chan Kwon Jung, Kennichi Kakudo, Moosa Khalil, Elham Khanafshar, Priyanthi Kumarasinghe, Ricardo V. Lloyd, Truong Phan-Xuan Nguyen, Idris Tolgay Ocal, Manju L. Prasad, Marc Pusztaszeri, Chanchal Rana, Peter M. Sadow, Dipti P. Sajed, Raja R. Seethala, Giovanni Tallini, Huy Gia Vuong, Gülçin Yeğen, Virginia A. LiVolsi, Yuri E. Nikiforov

Bibliographic record

VenueThe Journal of Clinical Endocrinology & Metabolism · 2024
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsMcGill UniversityUniversity of Calgary
Fundersnot available
KeywordsThyroid neoplasmMedicineConcordanceThyroid carcinomaFollicular phaseContext (archaeology)ThyroidRadiologyInternal medicineGeography

Abstract

fetched live from OpenAlex

CONTEXT: Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) was introduced as a new entity replacing the diagnosis of noninvasive encapsulated follicular variant of papillary thyroid carcinoma (PTC). Significant variability in the incidence of NIFTP diagnosed in different world regions has been reported. OBJECTIVE: To investigate the rate of adoption of NIFTP, change in practice patterns, and uniformity in applying diagnostic criteria among pathologists practicing in different regions. METHODS: Two surveys distributed to pathologists of the International Endocrine Pathology Discussion Group with multiple-choice questions on NIFTP adoption into pathology practice and whole slide images of 5 tumors to collect information on nuclear score and diagnosis. Forty-eight endocrine pathologists, including 24 from North America, 8 from Europe, and 16 from Asia/Oceania completed the first survey and 38 the second survey. RESULTS: A 94% adoption rate of NIFTP by the pathologists was found. Yet, the frequency of rendering NIFTP diagnosis was significantly higher in North America than in other regions (P = .009). While the highest concordance was found in diagnosing lesions with mildly or well-developed PTC-like nuclei, there was significant variability in nuclear scoring and diagnosing NIFTP for tumors with moderate nuclear changes (nuclear score 2) (case 2, P < .05). Pathologists practicing in North America and Europe showed a tendency for lower thresholds for PTC-like nuclei and NIFTP than those practicing in Asia/Oceania. CONCLUSION: Despite a high adoption rate of NIFTP across geographic regions, NIFTP is diagnosed more often by pathologists in North America. Significant differences remain in diagnosing intermediate PTC-like nuclei and respectively NIFTP, with more conservative nuclear scoring in Asia/Oceania, which may explain the geographic differences in NIFTP incidence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.316
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2024
Admission routes1
Has abstractyes

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