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Record W4313232287 · doi:10.1016/j.inpm.2022.100169

Using ultrasonography to improve fluoroscopic needle navigation in PM&R residents and medical students; a Randomised study

2022· article· en· W4313232287 on OpenAlexaff
Andrei Bursuc, Isabelle Denis, Christopher Mares, Johan Michaud, Cindy Nguyen

Bibliographic record

VenueInterventional Pain Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFluoroscopyMedicineSession (web analytics)UltrasoundMedical physicsUltrasonographyPhysical therapyRadiologyComputer science

Abstract

fetched live from OpenAlex

Background: Fluoroscopy guided interventions are widely used procedures in the treatment of musculoskeletal conditions. Understanding the movement of the needle is part of a resident's initial training when performing these procedures. Needle navigation training is largely gained with fluoroscopy. Objective: The purpose of this study is to determine whether the use of ultrasound training can lead to a decrease in time to reach a target under fluoroscopy. Methods: 32 medical students or residents. Exposure of one group of trainees to a practice session of needle navigation using ultrasound. The control group did not participate in ultrasound training. Time to reach the target during a fluoroscopy guided needle navigation test was measured in both groups. Results: The mean time to reach the target under fluoroscopy of the students unexposed to an ultrasound training (group 1 control group) was 183 ​s (standard deviation ​= ​160 ​s), while that after ultrasound training (group 2 experimental group) was 150,81 ​s (standard deviation ​= ​96 ​s) (p ​= ​0,483). Conclusion: Fluoroscopy needle navigation training was not improved by a 1-h group practice session with in-plane ultrasound needle navigation practice. Further studies need to be done with exposure for residents to a group practice session longer than 1-h.

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)0.009
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.428
Teacher spread0.375 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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