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Record W3154855659 · doi:10.1007/s00134-021-06394-2

Corticosteroids in COVID-19 and non-COVID-19 ARDS: a systematic review and meta-analysis

2021· review· en· W3154855659 on OpenAlexaff
Dipayan Chaudhuri, Kiyoka Sasaki, Aram Karkar, Sameer Sharif, Kimberly Lewis, Manoj J. Mammen, Paul Alexander, Zhikang Ye, Luis Enrique Colunga Lozano, Marie Warrer Munch, Anders Perner, Bin Du, Lawrence Mbuagbaw, Waleed Alhazzani, Stephen M. Pastores, John C. Marshall, François Lamontagne, Djillali Annane, G. Umberto Meduri, Bram Rochwerg

Bibliographic record

VenueIntensive Care Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeSt. Michael's HospitalSt. Joseph’s Healthcare HamiltonUniversity of TorontoJuravinski HospitalMcMaster UniversityImpact
FundersNational Cancer InstituteNovo Nordisk Fonden
KeywordsARDSMedicineCochrane LibraryMeta-analysisRandomized controlled trialMEDLINEPlaceboInternal medicineCoronavirus disease 2019 (COVID-19)AnesthesiologyIntensive care medicineAnesthesiaLungPathologyAlternative medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.015
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 score0.019
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.033
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.121
GPT teacher head0.424
Teacher spread0.303 · 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

Citations304
Published2021
Admission routes1
Has abstractno

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