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Record W4250876491 · doi:10.1186/s13756-021-00974-z

Abstracts from the 6th international conference on prevention & infection control (ICPIC 2021)

2021· article· en· W4250876491 on OpenAlexaff
Emily Puckett Rodgers, David W. Larson, Graeme Fridlay, H. Smart, T Penner, Michael G. Neuwirth, T Pommeranz, Frauke Mattner, Robin Otchwemah, ST-Y Yeh, Carlos G. Romo, Carroll E. Cross, R Andersen, Nicolas Loebel, Florian H. H. Brill, Thilo Burkard, Brandon Becker, Dajana Paulmann, Daniel Tödt, B. Bischoff, Eike Steinmann, Joerg Steinmann, David Brill, Sebastian Buhl, A. Stich, Clemens Bulitta

Bibliographic record

VenueAntimicrobial Resistance and Infection Control · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsMD Precision (Canada)
FundersConservatoire National des Arts et MétiersInstitut National de la Santé et de la Recherche Médicale
KeywordsMedicineInfection controlMedical microbiologyIntensive care medicineFamily medicineVirology

Abstract

fetched live from OpenAlex

Introduction: With the use of elastomeric-half mask respirators in healthcare settings, it has been reported that the verbal communication is decreased or compromised when wearing the masks (Palmiero, Symons, Morgan and Schaffer, 2016).This study examines the communication effectiveness of this innovation (elastomeric reusable respirator) and others in the industry, using speech intelligibility objective scoring, and qualitative research.Objectives: This research measured the acoustical performance, speech transmission index (STI) (Palmeiro, et al., 2016) on an innovative elastomeric respirator, and others utilized in health care, according to IEC 60268-16 Objective rating of speech intelligibility standard.Methods: STI measurements were obtained in a semi-anechoic acoustic test chamber/quiet room with background noise levels of less than 15 dBA.Then higher levels of background noise (57.6 dBA and 72 dBA, Zunn and Downey, 2005) was added to the test room and additional STI tests will be conducted in the presence of these elevated background noise levels.The "voice" signal was emitted by the artificial voice of an acoustic head and torso simulator (HATS) and was one of two types of sound: the STI test waveform or the the Harvard sentences sound waveform (phonetically balanced and very clearly spoken human speech).The background noise portion, when used, was added separately by a high fidelity loudspeaker.The speech and STI waveform sounds were produced inside the test room at the sound level of 60 dBA (1 m microphone distance).Results: In an environment with no background noise, the innovation of the re-usable elastomeric respirator, yielded the highest STI rating compared to other elastomeric respirators (0.90-0.91) or excellent rating.Other elastomeric respirators tested, showing fair, to low excellent range.The re-usable elastomeric respirator innovation had a 0.03 less in speech intelligibility than, single use N95 STI rating.Conclusion: Speech intelligibility is complex, and incorporates subjective (listener) criteria, objective speech intelligibility, and background noise, as well as the environment.Additional subjective testing, with the recorded sound files, with a randomized control clinical trial would benefit this research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.186
Threshold uncertainty score0.621

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0020.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.1860.057

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 head0.040
GPT teacher head0.366
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2021
Admission routes1
Has abstractyes

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