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Record W3034678323 · doi:10.1016/j.jaip.2020.06.001

Impact of COVID-19 on Pediatric Asthma: Practice Adjustments and Disease Burden

2020· article· en· W3034678323 on OpenAlexaff
Nikolaos G. Papadopoulos, Adnan Čustović, A. Deschildre, Alexander G. Mathioudakis, Wanda Phipatanakul, Gary Wong, Paraskevi Xepapadaki, Ioana Agache, Leonard B. Bacharier, Matteo Bonini, José A. Castro‐Rodríguez, Zhimin Chen, Timothy Craig, Francine M. Ducharme, Zeinab El-Sayed, Wojciech Feleszko, Alessandro Fiocchi, Luis García‐Marcos, James E. Gern, Anne Goh, René Maximiliano Gómez, Eckard Hamelmann, Gunilla Hedlin, Elham Hossny, Tuomas Jartti, Ömer Kalaycı, Alan Kaplan, Jon R. Konradsen, Piotr Kuna, Susanne Lau, Peter N. Le Souëf, Robert F. Lemanske, Mika J. Mäkelä, Mário Morais‐Almeida, Clare Murray, Karthik Nagaraju, Leyla S. Namazova-Baranova, Osman Yusuf, Paulo Márcio Pitrez, Petr Pohunek, César Fireth Pozo Beltrán, Graham Roberts, Arūnas Valiulis, Heather J. Zar, Rola Abou Taam, Hugo Azuara, Jacques Brouard, Pierrick Cros, Cindy De Lira, J.‐C. Dubus, Teija Dunder, Kamilla E. Efendieva, Carole Egron, Andrzej Emeryk, Yunuen R. Huerta Villalobos, Nidia Karen, P. Le Roux, Julia Levina, Monica Medley, Major Najaraju, Daniela Rivero‐Yeverino, Marja Ruotsalainen, Stanley J. Szefler, Cyril Schweitzer, Berenice Velasco Benhumea, Rosalaura Villarreal, Laurence Weiss, Anna Zawadzka‐Krajewska

Bibliographic record

VenueThe Journal of Allergy and Clinical Immunology In Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsInstitute of Infection and ImmunityUniversité de Montréal
FundersMerck Sharp and DohmeNovartis PharmaGenentechNational Institute of Allergy and Infectious DiseasesGrifolsNorges IdrettshøgskoleAstraZenecaAllergy TherapeuticsNational Institutes of HealthJapanese Association of Cardiac RehabilitationRegeneron PharmaceuticalsMylanNovartisBioCrystAmerican Academy of Allergy Asthma and ImmunologyChiesi FarmaceuticiCovis PharmaBoehringer IngelheimNovo NordiskCSL BehringNational Institute for Health and Care ResearchMedical Research CouncilTeva Pharmaceutical IndustriesAimmune TherapeuticsRespiratory Effectiveness GroupUniversity of KentuckyMedImmuneSanofiManchester Biomedical Research CentreMerckGlaxoSmithKlineFood Allergy Research and EducationPfizer
KeywordsCoronavirus disease 2019 (COVID-19)AsthmaMedicine2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicIntensive care medicineDiseaseVirologyInfectious disease (medical specialty)ImmunologyInternal medicineOutbreak

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)0.009
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.085
GPT teacher head0.476
Teacher spread0.391 · 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 designObservational
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

Citations169
Published2020
Admission routes1
Has abstractno

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