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Record W2129510058 · doi:10.2106/jbjs.l.01266

Development of a Cast Application Simulator and Evaluation of Objective Measures of Performance

2014· article· en· W2129510058 on OpenAlexaff
Joel Moktar, Charles A. Popkin, Andrew Howard, M. Lucas Murnaghan

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsSickKids Foundation
Fundersnot available
KeywordsComputer scienceSimulation

Abstract

fetched live from OpenAlex

BACKGROUND: Surgical simulation offers a low-risk learning environment with repetitive practice opportunities for orthopaedic residents. It is increasingly prevalent in many training programs, as acquisition of technical skills in the face of educational demands and reduced work hours becomes more challenging. In addition to surgical skills, orthopaedic residents must also learn the technique of cast application. Deficiencies in casting skill are risk factors for re-displacement of fractures and cast-specific complications. Formal educational models to instruct or to evaluate casting technique have not been well described or tested. The purposes of this study were to develop a cast application simulator and to validate a novel method of evaluating casting skill. METHODS: A module that simulates short arm cast application on a synthetic forearm model was developed. An Objective Structured Assessment of Technical Skill checklist was created with use of Delphi methodology involving nine content experts (five orthopaedic surgeons and four orthopaedic technologists). Nine participants (three medical students, three orthopaedic residents, two orthopaedic fellows, and one orthopaedic technologist) were used to evaluate the reliability and validity of the checklist. Nine de-identified videos of cast application were recorded and were utilized to test the newly developed Objective Structured Assessment of Technical Skill checklist and Modified Global Rating Scale for reliability and validity. Participants were grouped by training level (medical students, orthopaedic residents, and orthopaedic fellows or orthopaedic technologists) and were evaluated twice. RESULTS: Reliability was high as shown by intraclass correlation. The inter-rater reliability was 0.85 for the Objective Structured Assessment of Technical Skill, 0.81 for the Modified Global Rating Scale performance, and 0.78 for the Modified Global Rating Scale final product; the intra-rater reliability was 0.88 for the Objective Structured Assessment of Technical Skill, 0.85 for the Modified Global Rating Scale performance, and 0.81 for the Modified Global Rating Scale final product. The Objective Structured Assessment of Technical Skill checklist scores were 9.28 points for the medical students, 17.46 points for the orthopaedic residents, and 18.85 points for the orthopaedic fellows or orthopaedic technologists (p < 0.05, F = 6.32). The Modified Global Rating Scale performance and final product scores also reflected the level of training. Post hoc analysis showed a significant difference between the medical students and orthopaedic fellows or orthopaedic technologists for the Objective Structured Assessment of Technical Skill checklist and Modified Global Rating Scale. CONCLUSIONS: This casting simulation model and evaluation instrument is a reliable assessment of casting skill in applying a short arm cast. However, given the inability to stratify all three groups on the basis of the level of training, further work is needed to establish construct validity.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.152

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.093
GPT teacher head0.305
Teacher spread0.212 · 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

Citations37
Published2014
Admission routes1
Has abstractyes

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