Design and Development of a Lumbar Puncture Simulation Model
Bibliographic record
Abstract
Traditionally, medical trainees have been taught how to perform clinical skills on patients, moving directly from observation of senior physicians to carrying out the procedure themselves. In recent years, there has been a shift in medical education towards including simulation‐based teaching methods, which has been associated with reduced learner discomfort and improved patient outcomes. Simulation models are currently used to train medical students on lumbar punctures. The aim of this project is to improve upon existing models by creating a lumbar puncture simulation model with flexibility in the spine, with anatomically accurate physical measurements and mechanical properties, where required. Clinicians were interviewed to gather input on the initial design of the model, and will be interviewed again after testing the prototype to obtain feedback on how to further modify the model for increased clinical accuracy. A parametric CAD model was based on anatomic data collected by cadaveric experiments and measurements obtained from a literature search of imaging, cadaveric and in vivo studies. We will discuss the current prototype of the lumbar puncture model and the mechanical properties, physical measurements and clinician input incorporated in the design. Improved anatomic accuracy of the finalized model will allow trainees to translate their lumbar puncture skills more readily from practicing on the simulation model to performing on patients, leading to increased patient safety. Support or Funding Information Schulich ‐ UWindsor Opportunities for Research Excellence Program (SWORP) This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".