Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The laryngeal adductor reflex (LAR) is an important protective function of the larynx to prevent aspiration and subsequently, 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. In recent works, this device has been enhanced by adding stereoscopic vision and a mechatronic system for droplet formation control. Prior to extensive clinical trials, an experimental testing of prototype devices in a lab setting is highly desired. Furthermore, a demonstration of the method using a realistic phantom could increase patient compliance in a future clinical setting. For these purposes, a model of the human larynx including vocal fold adduction capabilities for LAR simulation was developed in this work. The combination of near real-time 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 data processing was performed based on high-speed recordings of the model reaction to compensate this factor. In the future, alternative methods for droplet impact detection could be explored and droplet impact intensity measurement capabilities could be added to the assembly presented here.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it