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Record W2003793897 · doi:10.1097/aap.0b013e318246f63c

The Creation of an Objective Assessment Tool for Ultrasound-Guided Regional Anesthesia Using the Delphi Method

2012· article· en· W2003793897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersUniversity of Toronto
KeywordsChecklistMedicineDelphiDelphi methodTrainerMedical physicsProcess (computing)Medical educationComputer scienceArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: The assessment of technical skills in ultrasound-guided regional anesthesia is currently subjective and relies largely on observations of the trainer. The objective of this study was to develop a checklist to assess training progress and to detect training gaps in ultrasound-guided regional anesthesia using the Delphi method. METHODS: A 30-item checklist was developed and then e-mailed to 18 reviewers for feedback. The checklist was modified on the basis of their feedback. This process of iteration was repeated until no further feedback was received, and a consensus was reached on the final composition of the checklist. A global rating scale (GRS) was introduced as a result of the feedback. RESULTS: Three rounds of feedback were required to reach consensus on the composition of the checklist and the GRS. The final checklist contains 22 items, and the GRS contains 9 categories. CONCLUSIONS: Using the Delphi method, a checklist and GRS were developed. These tools can serve as an objective means of assessing progress in ultrasound technical skills acquisition.

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.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.400
Teacher spread0.318 · 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