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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 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

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.027
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