An actuated larynx phantom for pre-clinical evaluation of droplet-based reflex-stimulating laryngoscopes
Bibliographic record
Abstract
Abstract The laryngeal adductor reflex (LAR) is an important protective function of the larynx to prevent aspiration and potentially fatal aspiration pneumonia by rapidly closing the glottis. Recently, a novel method for targeted stimulation and evaluation of the LAR has been proposed to enable non-invasive and reproducible LAR performance grading and to extend the understanding of this reflexive mechanism. The method relies on the laryngoscopically controlled application of accelerated water droplets in association with a high-speed camera system for LAR stimulation site and reflex onset latency identification. Prototype laryngoscopes destined for this method require validation prior to extensive clinical trials. Furthermore, demonstrations using a realistic phantom could increase patient compliance in future clinical settings. For these purposes, a model of the human larynx including vocal fold actuation for LAR simulation was developed in this work. The combination of image processing based on a custom algorithm and individual motorization of each vocal fold enables spatio-temporal droplet impact detection and controlled vocal fold adduction. To simulate different LAR pathologies, the current implementation allows to individually adjust the reflex onset latency of the ipsi- and contralateral vocal fold with respect to the automatically detected impact location of the droplet as well as the maximum adduction angle of each vocal fold. An experimental study of the temporal offset between desired and observed LAR onset latency due to image processing was performed for three average droplet masses based on highspeed recordings of the phantom. Median offsets of 100, 120 and 128 ms were found (n=16). This offset most likely has a multifactorial cause (image processing delay, inertia of the mechanical components, droplet motion). The observed offset increased with increasing droplet mass, as fluid oscillations after impact may have been detected as motion. In future work, alternative methods for droplet impact detection could be explored and the observed offset could be used for compensation of this undesirable delay.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".