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Record W4225947112 · doi:10.1183/13993003.02921-2021

Inhaled corticosteroids for outpatients with COVID-19: a meta-analysis

2022· review· en· W4225947112 on OpenAlexafffund
Todd C. Lee, Émilie Bortolussi‐Courval, Sara Belga, Nick Daneman, Adrienne K. Chan, Ryan Hanula, Nicole Ezer, Emily G. McDonald

Bibliographic record

VenueEuropean Respiratory Journal · 2022
Typereview
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreUniversity of British ColumbiaMcGill UniversityMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéMcGill University Health CentreMcGill University
KeywordsMedicineMeta-analysisCoronavirus disease 2019 (COVID-19)Inhaled corticosteroidsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakInternal medicineVirologyAsthma

Abstract

fetched live from OpenAlex

The role of inhaled corticosteroids for outpatient COVID-19 is evolving. Meta-analysis of reported clinical trials estimated probability of any effect for symptom resolution by day 14 at 100% and hospitalisation at 89.3%, respectively.https://bit.ly/3B2sDUi

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)0.004
metaresearch head score (Gemma)0.010
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 score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.025
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.219
GPT teacher head0.411
Teacher spread0.191 · 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 routes2
Has abstractyes

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