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Record W1997326164 · doi:10.1186/1472-6920-13-61

Arthroscopic proficiency: methods in evaluating competency

2013· article· en· W1997326164 on OpenAlexaff
Justin L. Hodgins, Christian Veillette

Bibliographic record

VenueBMC Medical Education · 2013
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of TorontoToronto Western Hospital
Fundersnot available
KeywordsArthroscopyCompetence (human resources)MEDLINEMedical physicsMedicinePsychomotor learningEducational measurementComputer scienceSurgeryPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: The current paradigm of arthroscopic training lacks objective evaluation of technical ability and its adequacy is concerning given the accelerating complexity of the field. To combat insufficiencies, emphasis is shifting towards skill acquisition outside the operating room and sophisticated assessment tools. We reviewed (1) the validity of cadaver and surgical simulation in arthroscopic training, (2) the role of psychomotor analysis and arthroscopic technical ability, (3) what validated assessment tools are available to evaluate technical competency, and (4) the quantification of arthroscopic proficiency. METHODS: The Medline and Embase databases were searched for published articles in the English literature pertaining to arthroscopic competence, arthroscopic assessment and evaluation and objective measures of arthroscopic technical skill. Abstracts were independently evaluated and exclusion criteria included articles outside the scope of knee and shoulder arthroscopy as well as original articles about specific therapies, outcomes and diagnoses leaving 52 articles cited in this review. RESULTS: Simulated arthroscopic environments exhibit high levels of internal validity and consistency for simple arthroscopic tasks, however the ability to transfer complex skills to the operating room has not yet been established. Instrument and force trajectory data can discriminate between technical ability for basic arthroscopic parameters and may serve as useful adjuncts to more comprehensive techniques. There is a need for arthroscopic assessment tools for standardized evaluation and objective feedback of technical skills, yet few comprehensive instruments exist, especially for the shoulder. Opinion on the required arthroscopic experience to obtain proficiency remains guarded and few governing bodies specify absolute quantities. CONCLUSIONS: Further validation is required to demonstrate the transfer of complex arthroscopic skills from simulated environments to the operating room and provide objective parameters to base evaluation. There is a deficiency of validated assessment tools for technical competencies and little consensus of what constitutes a sufficient case volume within the arthroscopy community.

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)0.006
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 score0.785
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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)0.0100.000

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 head0.097
GPT teacher head0.504
Teacher spread0.407 · 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

Citations50
Published2013
Admission routes1
Has abstractyes

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