The Effect of Augmented Reality Training on Percutaneous Needle Placement in Spinal Facet Joint Injections
Bibliographic record
Abstract
The purpose of this study was to determine if augmented reality image overlay and laser guidance systems can assist medical trainees in learning the correct placement of a needle for percutaneous facet joint injection. The Perk Station training suite was used to conduct and record the needle insertion procedures. A total of 40 volunteers were randomized into two groups of 20. 1) The Overlay group received a training session that consisted of four insertions with image and laser guidance, followed by two insertions with laser overlay only. 2) The Control group received a training session of six classical freehand insertions. Both groups then conducted two freehand insertions. The movement of the needle was tracked during the series of insertions. The final insertion procedure was assessed to determine if there was a benefit to the overlay method compared to the freehand insertions. The Overlay group had a better success rate (83.3% versus 68.4%, p=0.002), and potential for less tissue damage as measured by the amount of needle movement inside the phantom (3077.6 mm(2) versus 5607.9 mm(2) , p =0.01). These results suggest that an augmented reality overlay guidance system can assist medical trainees in acquiring technical competence in a percutaneous needle insertion procedure.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".