Augmented reality needle guidance improves facet joint injection training
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine if medical trainees would benefit from augmented reality image overlay and laser guidance in learning how to set the correct orientation of a needle for percutaneous facet joint injection. METHODS: A total of 28 medical students were randomized into two groups: (1) The Overlay group received a training session of four insertions with image and laser guidance followed by two insertions with laser overlay only; (2) The Control group was trained by carrying out six freehand insertions. After the training session, needle trajectories of two facet joint injections without any guidance were recorded by an electromagnetic tracker and were analyzed. Number of successful needle placements, distance covered by needle tip inside the phantom and procedural time were measured to evaluate performance. RESULTS: Number of successful placements was significantly higher in the Overlay group compared to the Control group (85.7% vs. 57.1%, p = 0.038). Procedure time and distance covered inside phantom have both been found to be less in the Overlay group, although not significantly. CONCLUSION: Training with augmented reality image overlay and laser guidance improves the accuracy of facet joint injections in medical students learning image-guided facet joint needle placement.
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 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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".