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Record W3144723382 · doi:10.1002/hed.26696

Development, translation, and preliminary validation of the neck dissection assessment tool

2021· article· en· W3144723382 on OpenAlexaff
Nathan Yang, Érika Mercier, Louis Guertin, Éric Bissada, Apostolos Christopoulos, Marie‐Jo Olivier, Jean‐Claude Tabet, Carlos M. Chiesa‐Estomba, Tareck Ayad

Bibliographic record

VenueHead & Neck · 2021
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsHôpital Maisonneuve-RosemontCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsIntraclass correlationChecklistReliability (semiconductor)MedicineDelphi methodOtorhinolaryngologyMedical physicsRating scaleDelphiPhysical therapyPsychometricsSurgeryPsychologyComputer scienceClinical psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: The objective was to develop an assessment tool to evaluate residents' competency for neck dissection and provide preliminary evidence of feasibility, reliability, and validity. METHODS: Six surgeons developed a neck dissection assessment tool using a modified Delphi method and evaluated 58 neck dissections from six junior and six senior otolaryngology residents. RESULTS: The assessment tool uses a double checklist: a previously validated global rating scale (GRS) and a task-specific checklist (TSC). Use of the instrument appeared feasible and the average scores on the GRS and TSC differed significantly between junior and senior residents. The Pearson correlation coefficient between both checklists was 0.87. Intraclass correlation (ICC) for inter-rater reliability was 0.69 for the GRS, and 0.80 for the TSC. CONCLUSION: This study provides preliminary evidence of feasibility, reliability, and validity for the first neck dissection assessment tool and provides a foundation for further psychometric analysis and research.

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)0.000
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 score0.388
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.052
GPT teacher head0.337
Teacher spread0.285 · 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
Published2021
Admission routes1
Has abstractyes

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