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Record W4403153407 · doi:10.1210/jendso/bvae163.2046

12210 Prognostic Determinants of Aggressive Histology Papillary Thyroid Carcinoma: A Pathway to Personalized Treatment

2024· article· en· W4403153407 on OpenAlexaff
M.Y. Almaghrabi, Ghassan Sindi, Henri Sasseville, S. Radi, Michael Tamilia

Bibliographic record

VenueJournal of the Endocrine Society · 2024
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsHistologyMedicineThyroid carcinomaPapillary carcinomaOncologyThyroidInternal medicineCarcinomaPathology

Abstract

fetched live from OpenAlex

Abstract Disclosure: M. Almaghrabi: None. G. Sindi: None. H. Sasseville: None. S. Radi: None. M. Tamilia: None. Background: Papillary thyroid carcinoma (PTC), the most common form of thyroid malignancy, generally presents with a favorable prognosis. However, aggressive variants of Papillary Thyroid Carcinoma (AV-PTC), such as tall cell, hobnail, columnar, and solid forms, pose significant challenges in prognosis and treatment, due to their unique pathological behaviors and increased risks of recurrence and mortality. The management debate for AV-PTC without invasive characteristics is ongoing. It's argued that aggressive histology alone should not dictate the clinical management of PTC patients lacking aggressive features. Objectives: This study aims to investigate the prognostic impact of invasive features in AV-PTC and their implications on clinical outcomes. Methods: We conducted a retrospective cohort study of AV-PTC patients treated from 2008 to 2022, analyzing medical records to identify prognostic factors related to disease recurrence, persistence, and overall survival. This involved distinguishing between groups with invasive characteristics—identified as one of the following: extrathyroidal extension, lymphovascular invasion, the presence of positive lymph nodes, or the detection of distant metastases upon diagnosis, and non-invasive AV-PTC groups. Results: Key findings from our comparison between invasive and non-invasive groups of AV-PTC highlighted significant factors associated with poorer outcomes in the invasive group. These included multifocality (P=0.001), larger tumor size (P=0.047), extra thyroid extension (P=0.002), lymph vascular invasion (P<0.001), the presence of contralateral nodules (P=0.018), and positive surgical margins (P=0.003). Moreover, advanced tumor stage and increased lymph node involvement were more common in invasive cases (P=0.012, P=0.040). Despite no significant differences in genetic mutations between groups (P=0.396), the Ki-67 index was numerically higher in the invasive group. Hemithyroidectomy followed by completion thyroidectomy, performed in 12 patients (22.2%). Survival analysis further highlighted the significance of these invasive features, with Kaplan-Meier curves indicating a 5-year freedom from recurrence rate of 77.18% and a 10-year rate of 68.61%. Univariable Cox regression revealed that the tumor size, positive margins, and tumor stage predicted recurrence. Seven patients had distant metastasis, and all were in the invasion group (18.92%) p= 0.088. Conclusion: Our study emphasizes the critical role of certain invasive features in determining the prognosis of AV-PTC. The adoption of more extensive treatments for patients without invasive features highlights the complexity of treating AV-PTC and the importance of personalized treatment plans. This research advances our understanding of AV-PTC, advocating for individualized approaches to enhance patient outcomes. Presentation: 6/3/2024

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)0.001
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.296
Teacher spread0.279 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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