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Record W3016463872 · doi:10.1016/j.ebiom.2020.102770

Sedating ventilated COVID-19 patients with inhalational anesthetic drugs

2020· letter· en· W3016463872 on OpenAlexafffundabout
Beverley A. Orser, Dian-Shi Wang, Wei‐Yang Lu

Bibliographic record

VenueEBioMedicine · 2020
Typeletter
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsHealth Sciences CentreUniversity of TorontoWestern UniversitySunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsMedicinePropofolMidazolamAnesthesiaMechanical ventilationAnestheticDexmedetomidinePneumoniaSedationHypoxemiaIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Most patients with COVID-19 exhibit mild to moderate respiratory symptoms; however, some develop severe pneumonia and hypoxemia is a frequent cause of death. Severely ill COVID-19 patients often require endotracheal intubation and mechanical ventilation. The choice of drugs to sedate these patients differs widely depending on drug availability and clinical expertise. We suggest that care providers with the appropriate clinical expertise, consider the use of inhalational anesthetic drugs, such as sevoflurane and isoflurane for the following reasons.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations19
Published2020
Admission routes3
Has abstractyes

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