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Record W4210768994 · doi:10.5935/0103-507x.20210081

Impact of liberal versus conservative saturation targets on gas exchange indices in COVID-19 related acute respiratory distress syndrome: a physiological study

2021· article· en· W4210768994 on OpenAlexaff
Javier Hernán Dorado, Joaquín Pérez, Emiliano Navarro, Emiliano Gogniat, Sebastián Escobar Torres, Sabrina Cagide, Matías Accoce

Bibliographic record

VenueCritical Care Science · 2021
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsImpact
Fundersnot available
KeywordsMedicineOxygenationRespiratory systemRespiratory physiologyMechanical ventilationAcute respiratory distressRespiratory distressCardiologyInternal medicineAnesthesiaLung

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare gas exchange indices behavior by using liberal versus conservative oxygenation targets in patients with moderate to severe acute respiratory distress syndrome secondary to COVID-19 under invasive mechanical ventilation. We also assessed the influence of high FiO2 on respiratory system mechanics. METHODS: We prospectively included consecutive patients aged over 18 years old with a diagnosis of COVID-19 and moderate-severe acute respiratory distress syndrome. For each patient, we randomly applied two FiO2 protocols to achieve SpO2 88% - 92% or 96%. We assessed oxygenation indices and respiratory system mechanics. RESULTS: We enrolled 15 patients. All the oxygenation indices were significantly affected by the FiO2 strategy (p < 0.05) selected. The PaO2/FiO2 deteriorated, PA-aO2 increased and Pa/AO2 decreased significantly when using FiO2 to achieve SpO2 96%. Conversely, the functional shunt fraction was reduced. Respiratory mechanics were not affected by the FiO2 strategy. CONCLUSION: A strategy aimed at liberal oxygenation targets significantly deteriorated gas exchange indices, except for functional shunt, in COVID-19-related acute respiratory distress syndrome. The respiratory system mechanics were not altered by the FiO2 strategy.Clinical Trials Register: NCT04486729.

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)0.003
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 score0.603
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.084
GPT teacher head0.427
Teacher spread0.343 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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