Randomized control trial of a holographic needle guidance technique for thoracic epidural placement
Bibliographic record
Abstract
INTRODUCTION: The Microsoft HoloLens is a head-mounted mixed reality device, which allows for overlaying hologram-like computer-generated elements onto the real world. This technology can be combined with preprocedural ultrasound during thoracic epidural placement to create a visual of the ideal needle angulation and trajectory in the users' field of view. This could result in a technically easier and potentially safer alternative to traditional blind landmark techniques. METHODS: Patients were randomly assigned to one of two groups: (1) HoloLens-assisted thoracic epidural technique (intervention-group H) or (2) traditional thoracic epidural technique (control-group C). The primary outcome was needling time (defined as skin puncture to insertion of epidural catheter) during the procedure. The secondary outcomes were number of needle punctures, number of needle movements, number of bone contacts, and epidural failure. Procedural pain and recovery room pain levels were also evaluated. RESULTS: Eighty-three patients were included in this study. The primary outcome of procedure time was reduced in the HoloLens group compared with control (4.5 min vs 7.3 min, p=0.02, 95% CI), as was the number of needle movements required (7.2 vs 14.4, p=0.01), respectively. There was no difference in intraprocedure or postprocedure pain, bone contacts, or total number of needle punctures. Three patients in the control group experienced epidural failure versus one patient in the HoloLens group. CONCLUSIONS: This study shows that thoracic epidural placement may be facilitated by using a guidance hologram and may be more technically efficient. TRIAL REGISTRATION NUMBER: NCT04028284.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.012 | 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".