MétaCan
Menu
Back to cohort
Record W4414146224 · doi:10.62547/mltr2588

Evaluating Medical Students' Confidence in Musculoskeletal Examination: Implications for Improving Musculoskeletal Medicine Education

2025· article· en· W4414146224 on OpenAlexaff
Mikayla L. Sonnleitner, Eli M. Snyder, H Lee, Kelli A Kokame, J Wong, Jaime C. Yu, Richard T. Kasuya, Henry L. Lew

Bibliographic record

VenueHawai‘i Journal of Health &amp Social Welfare · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInclusion (mineral)CurriculumPreparednessPhysical examinationLow ConfidenceMusculoskeletal disease

Abstract

fetched live from OpenAlex

In response to feedback from previous medical students, the office of medical education at a state-funded medical school (University of Hawaii) conducted an IRB-approved survey study to formally evaluate the experience of current medical students regarding their confidence with MSK examination skills, and solicited suggestions for improvement. We collected data from students who were transitioning from second to third year regarding the following: (1) confidence in various physical exams, (2) perceived preparedness for clerkships, (3) usefulness of existing MSK clinical activities, and (4) suggestions for improvement. A majority of students expressed lack of confidence in the MSK physical exam, which was notably lower than other organ system exams. Recommendations for curriculum improvement included early integration of MSK examination teaching with corresponding anatomy laboratory sessions, inclusion of physiatry teaching, and increased small-group learning sessions. This study revealed the need for (1) synchronizing MSK clinical skills training with anatomy curriculum during the first year, and (2) inclusion of physiatry teaching in the MSK curriculum. Ideally, this study will serve as a starting point for further innovations and improvements in MSK medical education.

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)0.007
metaresearch head score (Gemma)0.004
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 score0.949
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.039
GPT teacher head0.478
Teacher spread0.439 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueHawai‘i Journal of Health &amp Social WelfareSame topicMusculoskeletal Disorders and RehabilitationFrench-language works237,207