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Record W4401063023 · doi:10.1177/19160216241265687

Alternative Sources of Cautery in Thyroid Surgery and the Risk of Recurrent Laryngeal Nerve Injury: A Retrospective, Risk-Adjusted Analysis From the National Surgical Quality Improvement Program

2024· article· en· W4401063023 on OpenAlexafffund
Corliss Best, Jumana Hussain, Stephanie Johnson-Obaseki

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsUniversity of Ottawa
FundersOttawa Hospital Research Institute
KeywordsMedicineRecurrent laryngeal nerveSurgeryThyroidectomyRetrospective cohort studyThyroidNerve injuryConfoundingLogistic regressionGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

Objectives A risk-adjusted analysis was completed using data from the National Surgical Quality Improvement Program (NSQIP) to compare the rates of recurrent laryngeal nerve injury in thyroid surgery using traditional versus alternative sources of cautery (defined as Harmonic Scalpel © and LigaSure © ). Methods A retrospective cohort study was completed using the NSQIP database on adult patients who underwent total thyroidectomy, subtotal thyroidectomy, or completion thyroidectomy between 2016 and 2018. The primary outcome measure was recurrent laryngeal nerve injury. The exposure variable was use of conventional or alternative sources of cautery. Multivariable linear and logistic regression analyses were performed to control for potentially confounding variables. Results A total of 13,961 cases were analyzed; 9450 used alternative sources of cautery compared to 4511 where traditional cautery was used. There was no significant difference in rates of postoperative recurrent laryngeal nerve injury between the 2 sources of cautery compared. Conclusions Risk of recurrent laryngeal nerve injury should not be a factor when choosing method of cautery for thyroid surgery. Therefore, other factors like cost-effectiveness can be considered.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.015
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.318
Teacher spread0.291 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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