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Record W4391824250 · doi:10.1177/10556656241230882

Bilateral Cleft lip Simulation

2024· article· en· W4391824250 on OpenAlexaff
Jonathan Zaga-Galante, Raymond Tse, Richard A. Hopper, Anne Jurmu Arnold, David M. Fisher, Karen W.Y. Wong-Riff, Dale J. Podolsky

Bibliographic record

VenueThe Cleft Palate-Craniofacial Journal · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsFidelitySimulationLikert scaleMedicinePhysical therapySurgeryMedical physicsComputer sciencePsychology

Abstract

fetched live from OpenAlex

ObjectiveTo evaluate the features, anatomic accuracy, and educational value of a high-fidelity bilateral cleft lip simulator.DesignEvaluation of the simulator by expert cleft surgeons after performing a simulated bilateral cleft lip repair.SettingThe simulator was evaluated by the surgeons during the Latin American Craniofacial Association meeting.ParticipantsEleven experienced cleft surgeons evaluated the simulator. The cleft surgeons were selected based on their availability during the meeting.InterventionsThe participants performed a simulated bilateral cleft lip repair. They were each provided with a questionnaire assessing the simulator's features, realism and value as a training tool.Main Outcome Measure (s)The main outcome measure are the scores obtained from a Likert-type questionnaire assessing the simulators features, realism and value.ResultsOverall, the surgeons agreed with the simulator's realism and anatomic accuracy (average score of 3.7 out of 5). Overall, the surgeons strongly agreed with the value of the simulator as a training tool (average score of 4.6 out of 5).ConclusionsA high-fidelity bilateral cleft lip simulator was developed that is realistic and valuable as a training tool. The simulator provides a comprehensive training platform to gain hands-on experience in bilateral cleft lip repair before operating on real patients.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.021
GPT teacher head0.313
Teacher spread0.292 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

Explore more

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