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Record W3210684372 · doi:10.1101/2021.11.04.21265945

Inhaled Corticosteroids for Outpatients with Covid-19: A Meta-Analysis

2021· preprint· en· W3210684372 on OpenAlexaff
Todd C. Lee, Émilie Bortolussi‐Courval, Sara Belga, Nick Daneman, Adrienne K. Chan, Ryan Hanula, Nicole Ezer, Emily G. McDonald

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreUniversity of British ColumbiaMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Meta-analysisInhaled corticosteroidsMedicine2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Clinical trialEmergency medicineInternal medicineIntensive care medicineVirologyAsthma

Abstract

fetched live from OpenAlex

Abstract The role of inhaled corticosteroids for outpatient COVID-19 is evolving. We meta-analyzed reported clinical trials and estimated probability of any effect and number needed to treat of 50 or 20 for symptom resolution by day 14 [100%, 99.8%, 93.1%] and hospitalization [89.3%, 72.9%, 26.7%] respectively.

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)0.013
metaresearch head score (Gemma)0.025
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: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.033
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.003
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 head0.236
GPT teacher head0.474
Teacher spread0.239 · 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
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

Citations6
Published2021
Admission routes1
Has abstractyes

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