Epidural loss-of-resistance biomechanics: an open pilot cadaver study
Bibliographic record
Abstract
William P McKay1, Timothy Rosser1, Stefan Kriegler2, Adel Mohamed31Department of Anesthesia, 2Department of Radiology, 3Department of Anatomy, University of Saskatchewan, Saskatoon, CanadaPurpose: We measured dynamic biomechanics of loss-of-resistance (LOR) epidural placement in prone cadavers, focussing on the period immediately following LOR, to estimate forces acting on the tissue of the epidural space.Methods: An epidural syringe with 17G Hustead needle was instrumented to track force on the plunger, pressure in the chamber, and movement of barrel and plunger. Insertions were attempted in five formalin-preserved cadavers from T2–3 to L4–5, using LOR with saline or air, and confirmed with X-ray.Results: Sixteen insertions were successful. Soft tissues in formalin-preserved cadavers are much harder than in living humans. With continuous pressure on the plunger, fluid thrust through the needle at the point of LOR was significantly greater (P = 0.005) with saline (mean ± standard deviation [95% confidence intervals]: 19.3 ± 14.9 [8.3 to 30.3] N); than with air (0.17 ± 0.25 [0 to 0.39] N). Stress exerted on epidural tissue was similar (air = 7792 ± 920 [6986 to 8598] Pa; saline = 7378 ± 3019 [5141 to 9614] Pa); and in both cases was greater than the stress exerted by cerebrospinal fluid pushing outwardly on the dura (4800 Pa).Conclusion: Formalin-preserved cadavers are too stiff to make them an experimental model from which we can generalize to live humans, although we were successful in entering the epidural space and testing the instrumentation for further studies on live animals or humans. Continuous pressure on the plunger while advancing the epidural needle may “blow” the dura away from the needle tip and help prevent dural puncture. Better results are seen with saline rather than air.Keywords: epidural anesthesia, cadaver, spinal headache
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".