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Record W4390443937 · doi:10.1177/17534666231219630

Prone positioning during CPAP therapy in SARS-CoV-2 pneumonia: a concise clinical review

2023· article· en· W4390443937 on OpenAlexaff
Chiara Chiappero, Alessio Mattei, Luca Guidelli, Serena Millotti, Emiliano Ceccherini, Simon Oczkowski, Raffaele Scala

Bibliographic record

VenueTherapeutic Advances in Respiratory Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineContinuous positive airway pressureIntensive care medicineMechanical ventilationPneumoniaIntubationVentilator-associated pneumoniaARDSRespiratory physiologyVentilation (architecture)Positive airway pressureRespiratory failureCoronavirus disease 2019 (COVID-19)Intensive care unitAnesthesiaRespiratory systemInternal medicineLungDisease

Abstract

fetched live from OpenAlex

During the COVID-19 pandemic, the number of patients with hypoxemic acute respiratory failure (ARF) due to SARS-CoV-2 pneumonia threatened to overwhelm intensive care units. To reduce the need for invasive mechanical ventilation (IMV), clinicians tried noninvasive strategies to manage ARF, including the use of awake prone positioning (PP) with continuous positive airway pressure (CPAP). In this article, we review the patho-physiologic rationale, clinical effectiveness and practical issues of the use of PP during CPAP in non-intubated, spontaneously breathing patients affected by SARS-CoV-2 pneumonia with ARF. Use of PP during CPAP appears to be safe and feasible and may have a lower rate of adverse events compared to IMV. A better response to PP is observed among patients in early phases of acute respiratory distress syndrome. While PP during CPAP may improve oxygenation, the impact on the need for intubation and mortality remains unclear. It is possible to speculate on the role of PP during CPAP in terms of improvement of ventilation mechanics and reduction of strain stress.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.432
Teacher spread0.335 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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