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Record W4320001177 · doi:10.1016/j.jacadv.2022.100173

Liberation From Mechanical Ventilation in the Cardiac Intensive Care Unit

2023· article· en· W4320001177 on OpenAlexaff
Andi Shahu, Soumya Banna, Willard N. Applefeld, Penelope Rampersad, Carlos L. Alviar, Tariq Ali, Adriana Luk, Elaine Fajardo, Sean van Diepen, P. Elliott Miller

Bibliographic record

VenueJACC Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of AlbertaToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsDecompensationMechanical ventilationMedicineIntensive care medicineCoronary care unitIntensive care unitSedationRespiratory failureClinical PracticeAnesthesiaCardiologyNursingMyocardial infarction

Abstract

fetched live from OpenAlex

The prevalence of respiratory failure is increasing in the contemporary cardiac intensive care unit (CICU) and is associated with a significant increase in morbidity and mortality. For patients that survive their initial respiratory decompensation, liberation from invasive mechanical ventilation (IMV) and the decision to extubate requires careful clinical assessment and planning. Therefore, it is essential for the CICU clinician to know how to assess and manage the various stages of IMV liberation, including ventilator weaning, evaluation of extubation readiness, and provide post-extubation care. In this review, we provide a comprehensive approach to liberation from IMV in the CICU, including cardiopulmonary interactions relative to withdrawal from positive pressure ventilation, evaluation of readiness for and assessment of spontaneous breathing trials, sedation management to optimize extubation, strategies for patients at a high risk for extubation failure, and tracheostomy in the cardiovascular patient.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.027
GPT teacher head0.326
Teacher spread0.300 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

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Same venueJACC AdvancesSame topicIntensive Care Unit Cognitive DisordersFrench-language works237,207