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Record W4283736095 · doi:10.2196/34791

Freestyle Deliberate Practice Cadaveric Hand Surgery Simulation Training for Orthopedic Residents: Cohort Study

2022· article· en· W4283736095 on OpenAlexvenueno aff
Hannah James, Ross Fawdington

Bibliographic record

VenueJMIR Medical Education · 2022
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsCadaveric spasmOrthopedic surgeryMedicineSurgical simulationCohortPhysical therapyLearning curveIntervention (counseling)Training (meteorology)Medical educationSurgeryNursingComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Cadaveric simulation training may be part of the solution to reduced quantity and quality of operative surgical training in the modern climate. Cadaveric simulation allows the early part of the surgical learning curve to be moved away from patients into the laboratory, and there is a growing body of evidence that it may be an effective adjunct to traditional methods for training surgical residents. It is typically resource constrained as cadaveric material and facilities are expensive. Therefore, there is a need to be sure that any given cadaveric training intervention is maximally impactful. Deliberate practice (DP) theory as applied to cadaveric simulation training might enhance the educational impact. OBJECTIVE: The objectives of this study were (1) to assess the impact of a freestyle DP cadaveric hand surgery simulation training intervention on self-reported operative confidence for 3 different procedures and (2) to assess the subjective transfer validity, perceived educational value, and simulation fidelity of the training. METHODS: This study used validated questionnaires to assess the training impact on a cohort of orthopedic residents. The freestyle course structure allowed the residents to prospectively define personalized learning objectives, which were then addressed through DP. The study was conducted at Keele Anatomy and Surgical Training Centre, a medical school with an integrated cadaveric training laboratory in England, United Kingdom. A total of 22 orthopedic surgery residents of postgraduate year (PGY) 5-10 from 3 regional surgical training programs participated in this study. RESULTS: The most junior (PGY 5-6) residents had the greatest self-reported confidence gains after training for the 3 procedures (distal radius open reduction internal fixation, flexor tendon repair, ulnar shortening osteotomy), and these gains diminished with resident seniority. The confidence gains were proportional to the perceived procedural complexity, with the most complex procedure having the lowest pretraining confidence score across all experience levels, and the greatest confidence increase in posttraining. Midstage (PGY 7-8) residents reported receiving the highest level of educational benefit from the training but perceived the simulation to be less realistic, compared to either the junior or senior residents. The most senior residents (PGY 9-10) reported the greatest satisfaction with the self-directed, freestyle nature of the training. All groups reported that they were extremely likely to transfer their technical skill gains to their workplace, that they would change their current practice based on these skills, and that their patients would benefit as a result of their having undertaken the training. CONCLUSIONS: Freestyle, resident-directed cadaveric simulation provides optimum DP conditions whereby residents can target their individualized learning needs. By receiving intensive, directed feedback from faculty, they can make rapid skill gains in a short amount of time. Subjective transfer validity potential from the training was very high, and objective, quantitative evidence of this is required from future work.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.403
Teacher spread0.353 · 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.

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

Citations13
Published2022
Admission routes1
Has abstractyes

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