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Record W3110221461 · doi:10.29390/cjrt-2020-037

Cough peak flow to predict the extubation outcome: Comparison between three cough stimulation methods

2020· article· en· W3110221461 on OpenAlexvenueno aff
Christiane Melo Almeida, Agnaldo José Lopes, Fernando Silva Guimarães

Bibliographic record

VenueCanadian Journal of Respiratory Therapy · 2020
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnesthesiaReceiver operating characteristicSalineStimulationCutoffArea under the curveCatheterEndotracheal tubeSurgeryInternal medicineIntubation

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of this study was to compare the predictive value of three cough peak flow (CPF) maneuvers in predicting the extubation outcome in a cohort of mechanically ventilated subjects. METHODS: Eighty-one mechanically ventilated subjects who succeeded in the spontaneous breathing trial were included. In a randomized order, CPF was stimulated and measured using three methods: voluntary command (V_CPF), tracheal saline instillation (S_CPF), and mechanical stimulation with a catheter (C_CPF). Additionally, CPF was measured 20 min after the extubation (PE_CPF). The diagnostic accuracy of the CPF methods in relation to the extubation outcome was measured using the receiver operating characteristic (ROC) curve. ROC curve results were compared using the Hanley and McNeil method. RESULTS: = 0.13). The optimum cutoff values were V_CPF = 45 L/min, S_CPF = 60 L/min, and C_CPF = 55 L/min. PE_CPF also showed high accuracy in predicting the extubation outcome (AUC = 0.95; cutoff = 75 L/min). CONCLUSIONS: In mechanically ventilated and cooperative subjects, there is no difference in the accuracy of CPF measured voluntarily, with stimulation using saline or by catheter stimulation in predicting the reintubation. CPF recording after endotracheal tube removal has high accuracy to predict the extubation outcome.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.178
GPT teacher head0.398
Teacher spread0.220 · 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

Citations46
Published2020
Admission routes1
Has abstractyes

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