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Record W4409258831 · doi:10.1016/s2213-2600(25)00037-2

Inflammatory and clinical risk factors for asthma attacks (ORACLE2): a patient-level meta-analysis of control groups of 22 randomised trials

2025· review· en· W4409258831 on OpenAlexafffund
Fleur L. Meulmeester, Samuel Mailhot-Larouche, C.A. Celis-Preciado, Samuel Lemaire‐Paquette, Sanjay Ramakrishnan, Michael E. Wechsler, Guy Brusselle, Jonathan Corren, Jo Hardy, Sarah Diver, Christopher E. Brightling, Mario Castro, Nicola A. Hanania, David J. Jackson, Neil Martin, Annette Laugerud, Emilio Santoro, Chris Compton, Megan E Hardin, Cécile Holweg, A. Subhashini, Timothy Hinks, Richard Beasley, Jacob K. Sont, Ewout W. Steyerberg, Ian Pavord, Simon Couillard

Bibliographic record

VenueThe Lancet Respiratory Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersNational Health Service CorpsNIHR Oxford Biomedical Research CentreFonds de Recherche du Québec - SantéGenentechNational Institutes of HealthShionogiAcademy of Medical SciencesCanadian Lung AssociationStichting Astma BestrijdingUniversiteit LeidenRegeneron PharmaceuticalsNational Institute for Health and Care ResearchTeva Pharmaceutical IndustriesArrowhead PharmaceuticalsSanofiCelldex TherapeuticsAmgenPfizerEli Lilly and CompanyAstraZenecaPatient-Centered Outcomes Research Institute
KeywordsMedicineMeta-analysisAsthmaMEDLINERandomized controlled trialClinical trialInternal medicinePediatricsPhysical therapyIntensive care 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)0.016
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: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.025
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0220.039
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.291
GPT teacher head0.461
Teacher spread0.170 · 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

Citations73
Published2025
Admission routes2
Has abstractno

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