Using ultrasonography to improve fluoroscopic needle navigation in PM&R residents and medical students; a Randomised study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".