Introducing a Robotic Lumbar Puncture Simulator with Force Feedback: LP Simd
Bibliographic record
Abstract
Purpose: Lumbar Puncture (LP) is widely used for spinal and epidural anesthesia or Cerebrospinal fluid (CSF) sampling procedures. As this procedure is highly complicated and needs high experience to be performed correctly, it is necessary to teach this skill to the physicians. Considering the limitation of number of usage of rubber models and advantages of Virtual Reality (VR) environment for digital training of skills, we tried to investigate the capability of VR environment to train the LP procedures.with TLE. Materials and Methods: Geometrical model of the lumbar area of L2 to L5 are extracted from fusion of MR and CT imaging modalities. Also physical model of resistance of each layers against needle insertion at lumbar area are investigated through specially designed sensorized handle for LP needle and recorded from a 41-yearold female patient. Then geometrical and physical models of lumbar area are fused together and the Virtual Reality (VR) model of it, with insertion force rendering capability is extracted. Then the model is integrated with a haptic device and the complete VR environment is investigated. Results: In this work we introduced a robotic Lumbar Puncture Simulator (LP Sim) with force feedback which may be used for training the LP procedures .Using LP Sim, when the trainee insert the needle inside the lumbar area at the provided virtual reality environment, he/she may feel the insertion forces against his/her movement inside the lumbar area. Conclusion: The LP Sim is a virtual reality-enabled environment, with force feedback, that provides an appropriate framework for training this skill.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".