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Record W2417417334

Routine thyroidectomy in total laryngectomy: is it really indicated?

2009· article· en· W2417417334 on OpenAlexaffabout
Talal Al‐Khatib, Asher A. Mendelson, Karen Kost, Anthony Zeitouni, Martin J. Black, Richard J. Payne, Michael P. Hier

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineGynecologyLaryngectomySurgeryLarynx
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the incidence and nature of thyroid gland invasion (TGI) in laryngeal carcinoma at the head and neck centres at McGill University. METHOD: A retrospective case series was undertaken of 74 total laryngectomies performed at both McGill head and neck centres from 2001 to 2006. Thirteen specimens were excluded because thyroidectomies were not performed or laryngectomies were performed for nonprimary laryngeal carcinoma. Tumour stage, subsite, anatomic characteristics, and thyroid gland involvement were analyzed based on pathologic specimens. Pre- and postoperative radiation therapy treatment and thyroid function were also noted. RESULTS: Twenty supraglottic, 21 glottic, 15 transglottic, and 5 subglottic tumours were analyzed. Subglottic extension > 10 mm was noted in 22 specimens (36%). Cartilaginous invasion was noted in 37 of our specimens (61%), and lymph node metastasis was noted in 12 specimens (20%). One subglottic tumour demonstrated bilateral invasion of the thyroid gland and lymph node metastasis. Forty patients (54%) received preoperative radiation therapy and 34 patients (46%) received postoperative radiation therapy, with pre- and postoperative rates of hypothyroidism of 38.9% and 91%, respectively. CONCLUSION: TGI is rare for laryngeal cancer. Tumours with subglottic involvement or very advanced tumours may show a greater tendency for TGI over other tumours.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.258
Teacher spread0.234 · 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

Citations21
Published2009
Admission routes2
Has abstractyes

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