Validation of the soft-embalmed Thiel cadaver as a high-fidelity simulator of pressure during targeted nerve injection
Bibliographic record
Abstract
INTRODUCTION: Although administration of regional anesthesia nerve blocks has increased during the COVID-19 pandemic, training opportunities in regional anesthesia have reduced. Simulation training may enhance skills, but simulators must be accurate enough for trainees to engage in a realistic way-for example, detection of excessive injection pressure. The soft-embalmed Thiel cadaver is a life-like, durable simulator that is used for dedicated practice and mastery learning training in regional anesthesia. We hypothesized that injection opening pressure in perineural tissue, at epineurium and in subepineurium were similar to opening pressures measured in experimental animals, fresh frozen cadavers, glycol soft-fix cadavers and patients. METHODS: We systematically reviewed historical data, then conducted three validation studies delivering a 0.5 mL hydrolocation bolus of embalming fluid and recording injection pressure. First, we delivered the bolus at 12 mL/min at epimysium, perineural tissue, epineurium and in subepineurium at 48 peripheral nerve sites on three cadavers. Second, we delivered the bolus at using three infusion rates: 1 mL/min, 6 mL/min and 12 mL/min on epineurium at 70 peripheral nerve sites on five cadavers. Third, we repeated three injections (12 mL/min) at 24 epineural sites over the median and sciatic nerves of three cadavers. RESULTS: Mean (95%) injection pressure was greater at epineurium compared with subepineurium (geometric ratio 1.2 (95% CI: 0.9 to 1.6)), p=0.04, and perineural tissue (geometric ratio 5.1 (95% CI: 3.7 to 7.0)), p<0.0001. Mean (95%) injection pressure was greater at 12 mL/min compared with 1 mL/min (geometric ratio 1.6 (95% CI: 1.2 to 2.1), p=0.005). Pressure measurements were similar in study 3 (p>0.05 for all comparisons). DISCUSSION: We conclude that the soft-embalmed Thiel cadaver is a realistic simulator of injection opening pressure.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".