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Record W2920760515 · doi:10.1109/icrest.2019.8644482

Design of Piston-Driven Automated Cardiopulmonary Resuscitation Device with Patient Monitoring System

2019· article· en· W2920760515 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST) · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsConcordia University
Fundersnot available
KeywordsCardiopulmonary resuscitationHeart beatHeart rateMedicineResuscitationInternal medicineAnesthesiaBlood pressure

Abstract

fetched live from OpenAlex

Cardiopulmonary Resuscitation (CPR) is an emergency medical procedure applied when a patient's heart stops functioning suddenly, mainly due to cardiac arrest, drowning or electric shock. Normally, CPR is applied manually but the process is ineffective to provide adequate chest compressions to cardiac arrest patients. The main objective in this paper is to design a mechanized process of providing CPR and thus replacing the traditional manual CPR technique altogether. We designed an automated CPR device with piston-driven chest compression system and a patient monitoring system. The patient monitoring system consists of a heart rate monitor, body temperature monitor and respiratory rate monitor. The heart rate monitor is integrated with the automated CPR device. This means that when the heart rate monitor detects patient's heart beat then the automated CPR device will not operate but if the heart rate monitor detects no heart beat then the automated CPR device will immediately provide CPR to the patient. The automated CPR along with the patient monitoring system has been tested on a CPR manikin and the results of the performance is well within the accepted CPR guidelines.

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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.864
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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