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Record W2961612708 · doi:10.1016/j.cjco.2019.07.002

Use of Near-Infrared Spectroscopy by Paramedics During Out-of-Hospital Cardiac Arrest: A Feasibility Study

2019· article· en· W2961612708 on OpenAlexafffund
Ian R. Drennan, Joshua Gilgan, Karina Goncharenko, Steve Lin

Bibliographic record

VenueCJC Open · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersSt. Michael’s Hospital FoundationHeart and Stroke Foundation of Canada
KeywordsInfrared spectroscopyMedical emergencyEmergency medical servicesMedicineEmergency medicinePhysics

Abstract

fetched live from OpenAlex

Near-infrared spectroscopy (NIRS) provides continuous real-time measurement of regional cerebral oxygen saturation (rSO2) during resuscitation. We aimed to evaluate the feasibility of paramedics using NIRS during out-of-hospital cardiac arrest (OHCA) resuscitation. Paramedics were trained to record rSO2 and mark events during resuscitation. Feasibility was defined as > 70% of cases with rSO2 data and event markers. The monitor was applied on 23 patients with OHCA. Of these, 19 (83%) had rSO2 data (median duration of 17.9 minutes; interquartile range, 9.7-28) and 17 (74%) had event markers (median 3 events per case; interquartile range, 1-4). It is feasible for paramedics to apply NIRS during OHCA resuscitation.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.321
Teacher spread0.297 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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