Needle-free Mental Incisive Nerve Block: In vitro, Cadaveric, and Pilot Clinical Studies
Bibliographic record
Abstract
The present study aimed to optimize Needle-Free Liquid Jet Injection (NFLJI) for Mental Incisive Nerve Blocks (MINB) and evaluate its clinical safety and feasibility. A MINB protocol was developed and optimized by series of NFLJI experiments in soft tissue phantoms and cadavers, then validated in two pilot Randomized Controlled Trials (RCT). The NFLJI penetration depth was found to be directly proportional to the supply pressure and volume. High-pressure NFLJIs (620 kPa or above) created maximum force and total work significantly greater than needle injections. Low-pressure NFLJIs (413 kPa), however, produced results similar to those of needle injections. Additionally, high-pressure NFLJIs created jet impingement pressure and maximum jet penetration pressure higher than low-pressure NFLJIs. Pilot RCTs revealed that high-pressure NFLJI caused a high risk of discomfort (60%) and paresthesia (20%); meanwhile, low-pressure NFLJI was less likely to cause complications (0%). The preliminary success rates of MINB from cadavers using NFLJIs and needles were 83.3% and 87.5%. In comparison, those from RCTs are 60% and 70%, respectively. To conclude, NFLJI supply pressure can be adjusted to achieve effective MINB with minimal complications. Furthermore, the cadaver study and pilot RCTs confirmed the feasibility for further non-inferiority RCT.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".