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Record W2118863927 · doi:10.1186/1916-0216-42-48

Frozen section analysis and sentinel lymph node biopsy in well differentiated thyroid cancer

2013· article· en· W2118863927 on OpenAlexaff
Yelda Jozaghi, Keith Richardson, Sumeet Anand, Alex Mlynarek, Michael P. Hier, Véronique‐Isabelle Forest, Eyal Sela, Michael Tamilia, Derin Çağlar, Richard J. Payne

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsFrozen section procedureMedicineSentinel lymph nodeBiopsyNeck dissectionMetastasisThyroid carcinomaThyroid cancerCompartment (ship)LymphSurgeryThyroidCancerRadiologyPathologyCarcinomaInternal medicineBreast cancer

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study is to prospectively review the role of sentinel lymph node (SLN) biopsy in the management of well differentiated thyroid carcinoma (WDTC), and to determine the efficacy of intraoperative frozen section analysis at detecting SLN metastasis and central compartment involvement. METHODS: The SLN biopsy protocol using 1% methylene blue was performed in 300 patients undergoing thyroidectomy for WDTC. A limited pretracheal central compartment neck dissection (CCND) was performed on all patients. Lymph nodes staining blue were considered as SLN's. Both frozen and permanent section analyses were performed. RESULTS: SLN's with metastasis were found in 14.3% (43/300) of cases. Of this, 11% (33/300) were positive on intraoperative frozen section analysis. Frozen section results failed in predicting central compartment involvement in 15 cases (5%) whereas central neck compartment involvement was missed in 5 cases (1.7%) when based on permanent section results. On frozen section analysis, the sensitivity, specificity, positive predictive value and negative predictive value (95% CI) of our SLN biopsy technique aiming to remove all disease from the central compartment was 68.8% (53.6-80.9), 100% (98.1-100), 100% (87.0-100) and 94.4% (90.7-96.7) respectively with P < 0.0001. On permanent section analysis, the values were 89.6% (76.6-96.1), 100% (98.1-100), 100% (89.8-100), and 98.1% (95.3-99.3) with P < 0.0001. CONCLUSION: This data series demonstrates that patients with WDTC have positive SLN's in 14.3% of cases. Moreover, when the SLN's are negative for metastasis on frozen section, the central compartment was disease-free in 94.4% of cases. Finally, this study shows that 23.3% of positive SLN's were false negatives on intraoperative frozen section. According to this data, SLN involvement is an accurate predictor of central compartment metastasis, however surgeons should use caution when relying on intraoperative frozen section to determine whether to perform a CCND.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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

Citations34
Published2013
Admission routes1
Has abstractyes

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