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Record W2467702307

Laryngology in Canada: results of a national survey.

2012· article· en· W2467702307 on OpenAlexaffabout
Michael L McNeil, Timothy Brown

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLaryngologyLaryngoscopyMedicineScope (computer science)Medical educationGeneral surgerySurgeryComputer science
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Laryngology is a rapidly evolving and growing field in Canada. Recent technologies and trends, including endoscopic techniques, KTP and pulsed dye lasers, injection materials, and in-office procedures, are changing the nature and depth of services offered to patients. Our objective was to understand the current state of laryngology practice in Canada. DESIGN: Survey. SETTING: Adult laryngology practices in Canada. METHODS: We identified otolaryngologists working in Canada who self-identified as having a significant laryngology practice. We then asked them to complete an anonymous survey, with questions regarding composition of practice time, endoscopic equipment, electromyography, vocal cord medialization techniques, laser technologies, and plans for the future. MAIN OUTCOME MEASURES: Practice composition and use rates of available technology. RESULTS: Ten of 11 respondents reported that laryngology represents at least 20% of their practice (mean = 53%). All employed rigid laryngoscopy, stroboscopy, and exclusively CO2 laser. Six used distal chip endoscopes, whereas three perform transnasal esophagoscopy. CONCLUSIONS: The survey results demonstrate that laryngology practice in Canada is approaching state of the art and that most laryngologists plan to increase the breadth and scope of their practice as newer technologies become available. Limited funding and national regulations limit the adoption of some cutting-edge techniques.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.053
GPT teacher head0.256
Teacher spread0.203 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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