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Record W2754586014 · doi:10.1016/s0140-6736(17)30879-6

After asthma: redefining airways diseases

2017· review· en· W2754586014 on OpenAlexafffund
Ian Pavord, Richard Beasley, Àlvar Agustí, Gary P. Anderson, Elisabeth H. Bel, Guy Brusselle, Paul Cullinan, Adnan Čustović, Francine M. Ducharme, John V. Fahy, Urs Frey, Peter G. Gibson, Liam G. Heaney, Patrick G. Holt, Marc Humbert, Clare M. Lloyd, Guy B. Marks, Fernando D. Martínez, Peter D. Sly, Erika von Mutius, Sally E. Wenzel, Heather J. Zar

Bibliographic record

VenueThe Lancet · 2017
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversité de Montréal
FundersNational Health and Medical Research CouncilNational Institute of Allergy and Infectious DiseasesMedical Research CouncilGenentechNational Institutes of HealthMerck CanadaInternational Union Against Tuberculosis and Lung DiseaseWellcome TrustHealth Research Council of New ZealandF. Hoffmann-La RocheTeva Pharmaceutical IndustriesNational Institute for Health and Care ResearchMassachusetts Medical SocietyRegeneron PharmaceuticalsEuropean CommissionSanofiAstraZenecaNovartis Pharmaceuticals UK LimitedAmgenNational Heart, Lung, and Blood InstitutePfizerGlaxoSmithKline
KeywordsAsthmaMedicineSmall airwaysIntensive care medicineImmunology

Abstract

fetched live from OpenAlex

Asthma is responsible for considerable global morbidity and health-care costs. Substantial progress was made against key outcomes such as hospital admissions with asthma and mortality in the 1990s and early 2000s, but little improvement has been observed in the past 10 years, despite escalating treatment costs. New assessment techniques are not being adopted and new drug discovery has progressed more slowly than in other specialties.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.004

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 head0.105
GPT teacher head0.379
Teacher spread0.274 · 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 designNot applicable
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

Citations1,040
Published2017
Admission routes2
Has abstractyes

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