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Record W4281738338 · doi:10.2217/fvl-2021-0244

Efficacy and Safety of Corticosteroid Regimens for the Treatment of Hospitalized COVID-19 Patients: A meta-analysis

2022· review· en· W4281738338 on OpenAlexaff
Fangwen Zhou, Jiawen Deng, Kiyan Heybati, Qi Zuo, Saif Ali, Wenteng Hou, Chi Yi Wong, Harikrishnaa Ba Ramaraju, Oswin Chang, Thanansayan Dhivagaran, Zachary Silver

Bibliographic record

VenueFuture Virology · 2022
Typereview
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsMcGill UniversityCarleton UniversityMcMaster University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)MedicineMeta-analysis2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CorticosteroidVirologyInternal medicineIntensive care medicineOutbreakDisease

Abstract

fetched live from OpenAlex

Aim: To evaluate the efficacy and safety of corticosteroids for treating hospitalized COVID-19 patients. Materials & methods: Efficacy outcomes included time to negative SARS-CoV-2 tests, length of stay, duration and incidence of intensive unit care stay, incidence of mortality and duration and incidence of mechanical ventilation. Safety outcomes included the incidence of adverse events and severe adverse events, incidence of hyperglycemia and incidence of nosocomial infections. Results: Ninety-five randomized controlled trials (RCTs) and observational studies (n = 42,205) were included. Corticosteroids were associated with increased length of stay (based on RCT only), increased time to negative tests, decreased length of mechanical ventilation and increased odds of hyperglycemia. Conclusion: Corticosteroids should be considered in patients requiring mechanical ventilation, and glycemic monitoring may be needed when administering corticosteroids.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.020
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.085
GPT teacher head0.369
Teacher spread0.284 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations20
Published2022
Admission routes1
Has abstractyes

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