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Record W3139489079 · doi:10.18280/i2m.200102

Novel Handheld Device to Measure the Nasal Expiratory Flow Rate of the Two Nasal Cavities Individually and Simultaneously

2021· article· en· W3139489079 on OpenAlexvenueno aff
Thimira S. Thilakarathna, Mahesh Edirisinghe

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2021
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsnot available
FundersUniversity of Colombo
KeywordsAirflowBreathingAnemometerExhalationBiomedical engineeringMeasure (data warehouse)SimulationSpirometerMedicineComputer scienceMechanical engineeringEngineeringSurgeryAnesthesiaExhaled nitric oxidePhysicsAirwayMechanics

Abstract

fetched live from OpenAlex

This paper reveals a novel handheld device to measure the peak nasal expiratory flow rate of the two nasal cavities individually and simultaneously. Significantly, when using this device, the patient needs to inhale from the mouth and exhale from the nose, as a usual breath, and importantly there is no need to take extra effort to exhale. The key detection mechanism used is the anemometer technique and the measurement is done by using an optical wheel encoder and an ATmega328p microcontroller. The device is capable of measuring the exhaled airflow rate with an accuracy of ± 1 LPM. It also allows measuring the number of breaths per minute together with the respective temperature and the relative humidity of the exhaled airflow of the two nasal cavities separately. Two DHT22 sensor modules are used to measure relative humidity and temperature with an accuracy of ±1 %RH and ±0.5℃ respectively. This can be used by a Rhinologist as an initial diagnosing tool for several diseases, as the device allows them to get an idea about the patient’s exhaling behavior of the nasal airflow instantly. The device would be beneficial in the sports sector as a physical fitness indicator by means of the breathing style.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.045
GPT teacher head0.301
Teacher spread0.256 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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