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Record W2974118815 · doi:10.1515/cdbme-2019-0035

An actuated larynx phantom for pre-clinical evaluation of droplet-based reflex-stimulating laryngoscopes

2019· article· en· W2974118815 on OpenAlexaff
Jacob Friedemann Fast, Jiazhen He, Tobias Ortmaier, M. Jungheim, M. Ptok, Lüder A. Kahrs

Bibliographic record

VenueCurrent Directions in Biomedical Engineering · 2019
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsGlottisStroboscopeLarynxReflexImaging phantomMedicineVocal foldsSubglottisLatency (audio)Computer scienceOffset (computer science)SimulationNuclear medicineSurgeryPhysicsAnesthesia

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.442
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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