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Record W2058191247 · doi:10.2500/ajr.2008.22.3245

Construct Validation of a Low-fidelity Endoscopic Sinus Surgery Simulator

2008· article· en· W2058191247 on OpenAlexaff
Randy Leung, Jerry Leung, Allan Vescan, Adam Dubrowski, Ian Witterick

Bibliographic record

VenueAmerican Journal of Rhinology · 2008
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsThe Wilson CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineConstruct validityOtorhinolaryngologyCrossover studyConstruct (python library)Endoscopic sinus surgeryMedical physicsSimulationTask (project management)CurriculumFidelitySurgeryComputer sciencePatient satisfactionSystems engineering

Abstract

fetched live from OpenAlex

BACKGROUND: Before a simulator becomes widely accepted, it must be relevant, affordable, and accessible. We have developed a low-cost model emphasizing the basic skills required for endoscopic sinus surgery (ESS). It is noninvasive, free from risk of infection, and an excellent low-pressure learning opportunity. The current study was designed to assess the construct validity of our simulator. METHODS: We conducted a stratified randomized crossover-control study. Otolaryngology residents, fellows, and faculty performed predetermined tasks on the model or cadaver, and then switched. Evaluation included hand motion analysis, task time, and blinded expert review. RESULTS: Sixteen subjects at various levels of training participated. Cadaver performance correlated well with level of training and previous experience with ESS. However, model performance did not demonstrate statistically significant correlation. CONCLUSION: Our model was unable to demonstrate clear construct validity at this time. Materials and structural modifications are in progress. Pending further validation, its low-cost construction possesses potential for integration into otolaryngology residency curricula. Assessment of the simulator's ability to improve surgical skill is also planned.

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)0.001
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 score0.063
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.034
GPT teacher head0.297
Teacher spread0.264 · 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

Citations36
Published2008
Admission routes1
Has abstractyes

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