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Record W4385658592 · doi:10.36834/cmej.77293

Assessing the effectiveness of a cadaveric workshop in improving resident physicians’ confidence in performing ultrasound-guided joint injections

2023· article· en· W4385658592 on OpenAlexafffundvenue
Jane S Thornton, Ahmed Mahdi, Lydia Schultz, Graham Briscoe

Bibliographic record

VenueCanadian Medical Education Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsFowler Kennedy Sport Medicine ClinicWestern University
FundersSchulich School of Medicine and Dentistry
KeywordsCadaveric spasmMedicineUltrasoundJoint (building)CadaverMedical physicsMedical educationRadiologySurgeryEngineering

Abstract

fetched live from OpenAlex

Our study showed that an educational workshop using a cadaveric specimen is effective in improving resident physicians' confidence in performing ultrasound-guided, as well as landmark-based, joint injections. Participants also reported a strong interest in future cadaveric workshops on ultrasound-guided joint injections and believe such workshops would be a valuable educational resource for other physicians. Implementing cadaver-based ultrasound-guided joint injection workshops similar to the one discussed in this manuscript could be used to supplement training for these procedures in medical education and provide residents with the early experience they need to be able to perform these injections independently in clinic settings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.034
GPT teacher head0.371
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes3
Has abstractyes

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