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Record W4388595051 · doi:10.1002/hed.27573

Incidence and complications of hypothyroidism postlaryngectomy: A systematic review and <scp>meta‐analysis</scp>

2023· review· en· W4388595051 on OpenAlexaff
Norbert Banyi, Jae Young Kwon, Sena Turkdogan, Thomas D. Milner, Eitan Prisman

Bibliographic record

VenueHead & Neck · 2023
Typereview
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineLaryngectomyIncidence (geometry)Radiation therapyThyroidectomySurgeryComplicationMeta-analysisThyroidInternal medicineLarynx

Abstract

fetched live from OpenAlex

BACKGROUND: Hypothyroidism is common postlaryngectomy and is associated with laryngectomy-specific complications. The objective of this study is to determine the incidence and predictors of hypothyroidism postlaryngectomy and its associated complications. METHODS: Systematic review, data extraction, and meta-analyses were performed following the PRISMA protocol. Six databases were searched for studies reporting on postlaryngectomy thyroid status with incidence, risk factors, management, or complications. RESULTS: Fifty-one studies with 6333 patients were included. The pooled incidence of postlaryngectomy hypothyroidism is 49% (CI 42%-57%). Subgroup analysis showed postlaryngectomy hypothyroidism rates significantly correlated with hemithyroidectomy and radiotherapy. Patients who underwent laryngectomy, hemithyroidectomy, and radiotherapy had a 65% (CI 59%-71%) rate of hypothyroidism; laryngectomy and hemithyroidectomy 46% (CI 33%-60%); laryngectomy and radiotherapy 26% (CI 19%-35%); and laryngectomy alone 11% (CI 4%-27%) (p < 0.001). CONCLUSIONS: Laryngectomized patients with partial thyroidectomy or radiation therapy are at significant risk of postoperative hypothyroidism. Evidence-based protocols for early detection and (prophylactic) treatment should be established.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0110.002
Bibliometrics0.0010.002
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 head0.124
GPT teacher head0.377
Teacher spread0.253 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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