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Noninvasive Monitoring of End-Tidal Carbon Dioxide in the Emergency Department

2006· article· en· W2331570423 on OpenAlexaff
Nicki Gilboy, M. R. S. Hawkins

Bibliographic record

VenueAdvanced Emergency Nursing Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsCapnographyMedicineEmergency departmentMedical emergencyEndotracheal tubeCarbon dioxideVentilation (architecture)Emergency medicineIntensive care medicineAnesthesiaNursingIntubationEngineering

Abstract

fetched live from OpenAlex

Noninvasive monitoring of end-tidal carbon dioxide (ETCO2) is not new technology but its routine use in the emergency department is a recent development. It is a better tool to evaluate ventilation when compared to oximetry because it provides the caregiver with breath-to-breath information. End-tidal carbon dioxide reflects the production, transportation, and elimination of CO2. This technology has been used to evaluate endotracheal tube placement. Now with both side stream and mainstream monitoring available, emergency departments can use ETCO2 in a variety of situations. The emergency nurse needs to be able to evaluate the configuration of the waveform in addition to the numeric value.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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 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
Published2006
Admission routes1
Has abstractyes

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