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Record W3138211593 · doi:10.1186/s13054-021-03491-y

Expert consensus statements for the management of COVID-19-related acute respiratory failure using a Delphi method

2021· article· en· W3138211593 on OpenAlexaff
Prashant Nasa, Élie Azoulay, Ashish K. Khanna, Ravi Jain, Sachin Gupta, Yash Javeri, Deven Juneja, Pradeep Rangappa, Krishnaswamy Sundararajan, Waleed Alhazzani, Massimo Antonelli, Yaseen M. Arabi, Jan Bakker, Laurent Brochard, Adam M. Deane, Bin Du, Sharon Einav, Ognjen Gajic, Samuel M. Galvagno, Claude Guérin, Samir Jaber, Gopi C. Khilnani, Younsuck Koh, Jean-Baptiste Lascarrou, F.S. Machado, Manu L. N. G. Malbrain, Jordi Mancebo, Michael T. McCurdy, Brendan McGrath, Sangeeta Mehta, Armand Mekontso Dessap, Mervyn Mer, Michael Nurok, Pauline K. Park, Paolo Pelosi, John V. Peter, Jason Phua, David Pilcher, Lise Piquilloud, Peter Schellongowski, Marcus J. Schultz, Manu Shankar‐Hari, Suveer Singh, Massimiliano Sorbello, Ravindranath Tiruvoipati, Andrew Udy, Tobias Welte, Sheila Nainan Myatra

Bibliographic record

VenueCritical Care · 2021
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSinai Health SystemSt. Michael's HospitalUniversity of TorontoMcMaster University
FundersNational Institutes of HealthBundesministerium für Bildung und ForschungDepartment of Health and Social CareMSD ItaliaNational Institute for Health and Care ResearchSanofiPfizerAstraZenecaDeutsche ForschungsgemeinschaftEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineARDSIntensive care medicineIntubationRespiratory failureVentilation (architecture)HypoxemiaAnesthesiaIntensive careLungInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented pressure on healthcare system globally. Lack of high-quality evidence on the respiratory management of COVID-19-related acute respiratory failure (C-ARF) has resulted in wide variation in clinical practice. Methods Using a Delphi process, an international panel of 39 experts developed clinical practice statements on the respiratory management of C-ARF in areas where evidence is absent or limited. Agreement was defined as achieved when > 70% experts voted for a given option on the Likert scale statement or > 80% voted for a particular option in multiple-choice questions. Stability was assessed between the two concluding rounds for each statement, using the non-parametric Chi-square ( χ 2 ) test ( p < 0·05 was considered as unstable). Results Agreement was achieved for 27 (73%) management strategies which were then used to develop expert clinical practice statements. Experts agreed that COVID-19-related acute respiratory distress syndrome (ARDS) is clinically similar to other forms of ARDS. The Delphi process yielded strong suggestions for use of systemic corticosteroids for critical COVID-19; awake self-proning to improve oxygenation and high flow nasal oxygen to potentially reduce tracheal intubation; non-invasive ventilation for patients with mixed hypoxemic-hypercapnic respiratory failure; tracheal intubation for poor mentation, hemodynamic instability or severe hypoxemia; closed suction systems; lung protective ventilation; prone ventilation (for 16–24 h per day) to improve oxygenation; neuromuscular blocking agents for patient-ventilator dyssynchrony; avoiding delay in extubation for the risk of reintubation; and similar timing of tracheostomy as in non-COVID-19 patients. There was no agreement on positive end expiratory pressure titration or the choice of personal protective equipment. Conclusion Using a Delphi method, an agreement among experts was reached for 27 statements from which 20 expert clinical practice statements were derived on the respiratory management of C-ARF, addressing important decisions for patient management in areas where evidence is either absent or limited. Trial registration : The study was registered with Clinical trials.gov Identifier: NCT04534569.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.570
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.144
GPT teacher head0.491
Teacher spread0.347 · 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 designBench or experimental
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

Citations194
Published2021
Admission routes1
Has abstractyes

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