Stereo Laryngoscopic Impact Site Prediction for Droplet-Based Stimulation of the Laryngeal Adductor Reflex
Bibliographic record
Abstract
The laryngeal adductor reflex (LAR) is a vital reflex of the human larynx. LAR malfunctions may cause life-threatening aspiration events. An objective, noninvasive, and reproducible method for LAR assessment is still lacking. Stimulation of the larynx by droplet impact, termed Microdroplet Impulse Testing of the LAR (MIT-LAR), may remedy this situation. However, droplet instability and imprecise stimulus application thus far prevented MIT-LAR from gaining clinical relevance. We present a system comprising two alternative, custom-built stereo laryngoscopes, each offering a distinct set of properties, a droplet applicator module, and image/point cloud processing algorithms to enable a targeted, droplet-based LAR stimulation. Droplet impact site prediction (ISP) is achieved by droplet trajectory identification and spatial target reconstruction. The reconstruction and ISP accuracies were experimentally evaluated. Global spatial reconstruction errors at the glottal area of (0.3±0.3) mm and (0.4±0.3) mm and global ISP errors of (0.9±0.6) mm and (1.3±0.8) mm were found for a rod lens-based and an alternative, fiberoptic laryngoscope, respectively. In the case of the rod lens-based system, 96% of all observed ISP error values are inferior to 2 mm; a value of 80% was found with the fiberoptic assembly. This contribution represents an important step towards introducing a reproducible and objective LAR screening method into the clinical routine.
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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 | 0.000 | 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".