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Record W3194137933 · doi:10.1177/01945998211033523

Role of Thyroidectomy in Recurrent Laryngeal Carcinoma.

2022· article· en· W3194137933 on OpenAlexaff

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsSt. Thomas Hospital
FundersNational Institute for Health and Care Research
KeywordsThyroidectomyLaryngectomyRadiological weaponTotal thyroidectomySubtotal thyroidectomyLarynx

Abstract

fetched live from OpenAlex

OBJECTIVE: Management of recurrent laryngeal cancer presents a major challenge, and salvage laryngectomy is complicated by previous oncologic treatments. Thyroidectomy as part of salvage laryngectomy adds a nonnegligible degree of morbidity. The purpose of this study is to assess the rate of thyroid gland invasion in patients undergoing salvage laryngectomy to determine relevant predictive factors. STUDY DESIGN: Case series with chart review. SETTING: Department of Otorhinolaryngology, Head and Neck Surgery, Guy's Hospital, London, United Kingdom. METHODS: A retrospective review of patients undergoing salvage laryngectomy between 2009 and 2019 was undertaken. Preoperative cross-sectional imaging and histopathological analysis were performed to define evidence and predictors of thyroid gland invasion (TGI). RESULTS: Fifty-one patients had salvage laryngectomy. Histological evidence of TGI was found in 4 patients (7.8%). No significant relationship was found between histological TGI and subsite of primary carcinoma, degree of differentiation, T staging, or radiological TGI. Preoperative computed tomography had a high negative predictive value for TGI. CONCLUSION: Thyroidectomy should be carefully considered in patients undergoing salvage laryngectomy, and its extent should be defined on an individual basis. Total thyroidectomy should not routinely be performed in salvage laryngectomy or pharyngolaryngectomy in patients with no preoperative radiological evidence of TGI on cross-sectional imaging, unless there is intraoperative evidence of TGI.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.220
Teacher spread0.202 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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