Use of 3D Printing Technology to Create a Canine Simulator for Cerebrospinal Fluid Sampling at the Lumbar Subarachnoid Space
Bibliographic record
Abstract
Cerebrospinal fluid (CSF) sampling at the lumbar subarachnoid space (LSS) is technically challenging to learn. Currently, training relies on cadaver availability or performance in a clinical scenario. This study aims to develop and validate a low-cost, high-fidelity simulator to train in this technique. Using three-dimensional printing technology, a model of a healthy adult dog’s lumbosacral vertebral column was produced. The model was augmented with synthetic materials and a fluidic system to replicate all procedural steps and permit successful CSF collection. The simulator was validated by four experts, who rated it highly across multiple criteria. Final-year veterinary students were recruited to take part in practical sessions using either the simulator ( n = 16) or a cadaver ( n = 16). Performance was recorded for each student, and feedback was obtained using an anonymous online survey. Student performance was similar between groups ( p = .2), with 87.5% and 68.75% of students in the simulator and cadaver group, respectively, successfully placing the needle into the LSS. All successful students in the simulator group were able to obtain a CSF sample, compared with none in the cadaver group. No difference in the number of attempts was detected between groups ( p > .99), with most students taking more than three attempts. User experience was similar between groups, with 93.8% of students in each group rating the session as a positive learning experience. In summary, we demonstrate the validity of a novel, low-cost, and anatomically precise simulator that can be used for teaching CSF sampling at the LSS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".