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Record W2268832531 · doi:10.3233/978-1-60750-706-2-36

Anesthesia Residents' Preference for Learning Interscalene Brachial Plexus Block (ISBPB): Traditional Winnie's Technique vs. Ultrasound-Guided Technique

2011· article· en· W2268832531 on OpenAlexaff
Imad T. Awad, Ewen W. Chen, Colin J. L. McCartney, Jeffrey J. H. Cheung, Adam Dubrowski

Bibliographic record

VenueStudies in health technology and informatics · 2011
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsCurriculumModalitiesMedicineModality (human–computer interaction)Treatment modalityAnesthesiaMedical educationUltrasoundPsychologyComputer scienceSurgeryPedagogyRadiologySociologyArtificial intelligence

Abstract

fetched live from OpenAlex

There is a recent shift from traditional nerve stimulation (NS) to ultrasound-guided (UG) techniques in regional anesthesia (RA). This shift prompted educators to readdress the best way to teach these two modalities. Development of a more structured curriculum requires an understanding of student preferences and perceptions. To help in structuring the teaching curriculum of RA, we examined residents' preferences to the methods of instruction (NS Vs. UG techniques). Novice residents (n=12) were enrolled in this parallel crossover trial. Two groups of 6 residents received a didactic lecture on NS or UG techniques. The groups then crossed over to view the other lecture. After they observed a demo of ISBPB on two patients using NS and US. The residents completed a questionnaire regarding their impression of each technique and the learning experience. UG technique was perceived to be safer and to have more educational value than NS. However, residents felt both techniques should be mandatory in the teaching curriculum.

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)0.002
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
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)0.0020.000

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 head0.107
GPT teacher head0.333
Teacher spread0.226 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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