Droplet applicator module for reproducible and controlled endoscopic laryngeal adductor reflex stimulation
Bibliographic record
Abstract
This work presents a droplet applicator module to generate stable droplets with different muzzle energies for the reproducible endoscopic stimulation of the laryngeal adductor reflex (LAR). The LAR is a protective reflex of the human larynx; an abnormal LAR performance may cause aspiration pneumonia. A pathological LAR can be detected by evaluating its onset latency. The reflex can be triggered by shooting a droplet onto the laryngeal mucosa, which is referred to as Microdroplet Impulse Testing of the LAR (MIT-LAR). Stimulation intensity variation is desired as the reflex threshold may vary inter-individually. The kinetic energy of a droplet after detachment from the nozzle, i.e., its muzzle energy, is considered an appropriate metric for the LAR stimulation intensity. In this work, a suitable nozzle channel geometry is identified based on the experimental evaluation of droplet formation using three different nozzle channel geometries. Two nontoxic additives are evaluated regarding their effect on fluid properties and droplet formation. The range of achievable droplet muzzle energies is determined by high-speed cinematography in association with a physically motivated model of the macroscopic droplet motion. The experimental results show that sodium chloride is a suitable additive to enhance droplet stability in the studied parameter range with the proposed system. Droplet muzzle energy variation from 0.02 μJ to 1.37 μJ was achieved while preserving the formation of a single stimulation droplet. These results are an important prerequisite for a safe and reproducible LAR stimulation by MIT-LAR, which could also help to further elucidate the physiological mechanisms underlying this laryngeal reflex.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".