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Record W2613512051 · doi:10.1002/hed.24722

Perioperative practices in thyroid surgery: An international survey

2017· article· en· W2613512051 on OpenAlexaff
Anastasios Maniakas, Apostolos Christopoulos, Éric Bissada, Louis Guertin, Marie‐Jo Olivier, Jacques Malaise, Tareck Ayad

Bibliographic record

VenueHead & Neck · 2017
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsCentre Hospitalier de l’Université de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineThyroidOtorhinolaryngologySpecialtyGeneral surgeryEndocrine surgeryPerioperativeSurgeryHead and neck surgeryFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Perioperative practices in thyroid surgery vary from one specialty, institution, or country to the next. We evaluated the preoperative, intraoperative, and postoperative practices of thyroid surgeons focusing on preoperative ultrasound, vocal cord evaluation, wound drains, and hospitalization duration, among others. METHODS: A survey was sent to 7 different otolaryngology and endocrine/general surgery associations. RESULTS: There were 965 respondents from 52 countries. Surgeon-performed ultrasound is practiced by more than one third of respondents. Otolaryngologists perform preoperative and postoperative vocal cord evaluation more often than endocrine/general surgeons (p < .001). Sixty percent of respondents either never place drains or place drains <50% of the time in thyroid lobectomies (43% for total thyroidectomies). Outpatient thyroid surgery is most frequently performed by surgeons in the United States (63%). CONCLUSION: This epidemiologic study is the first global thyroid survey of its kind and clearly demonstrates the variability and evolving trends in thyroid surgery. © 2017 Wiley Periodicals, Inc. Head Neck 39: 1296-1305, 2017.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.159
GPT teacher head0.420
Teacher spread0.261 · 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.

Study designObservational
DomainMethods
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

Citations19
Published2017
Admission routes1
Has abstractyes

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