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Record W4416097386 · doi:10.1111/cea.70173

Assessment of the Effectiveness of Allergic Rhinitis Medications Using a Target Trial Emulation Approach Based on Mobile Health Data

2025· article· en· W4416097386 on OpenAlexaff
Nuno Lourenço‐Silva, Bernardo Sousa‐Pinto, Antonio Bognanni, Matteo Martini, Michał Ordak, Giovanni Paoletti, Sara Gil‐Mata, Rita Amaral, Anna Bedbrook, Patrizia Bonadonna, Luisa Brussino, Giorgio Walter Canonica, João Coutinho‐Almeida, Álvaro A. Cruz, Mark S. Dykewicz, Mattia Giovannini, Bilun Gemicioğlu, Juan Carlos Ivancevich, Ludger Klimek, Violeta Kvedarienė, Désirée Larenas‐Linnemann, Manuel Marques‐Cruz, André Moreira, Marek Niedoszytko, Ana Margarida Pereira, Nikolaos G. Papadopoulos, N. Pham‐Thi, Frederico S. Regateiro, Sanna Toppila‐Salmi, Bolesław Samoliński, J. Sastre, Luís Taborda‐Barata, Tuuli Thomander, Ilgım Vardaloğlu, Arūnas Valiulis, Leticia de las Vecillas, Maria Teresa Ventura, Jolanta Walusiak‐Skorupa, Yi‐Kui Xiang, Oliver Pfaar, João Fonseca, Torsten Zuberbier, Holger J. Schünemann, Danilo Di Bona, Rafael José Vieira

Bibliographic record

VenueClinical & Experimental Allergy · 2025
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsMcMaster University
FundersDirectorate-General for Communications Networks, Content and TechnologyUniversidade do PortoFilhaTampereen TuberkuloosisäätiöGlaxoSmithKlineUniversité de LiègeMylanCelltrionCentro de Investigação em Tecnologias e Serviços de SaúdeCelldex Therapeutics
KeywordsmHealthClinical trialRandomized controlled trialMEDLINEConfoundingFingolimodCausal inferenceEmulationDigital health

Abstract

fetched live from OpenAlex

176

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)0.391
metaresearch head score (Gemma)0.560
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.391
Threshold uncertainty score0.751

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3910.560
Meta-epidemiology (narrow)0.0070.003
Meta-epidemiology (broad)0.0120.020
Bibliometrics0.0080.006
Science and technology studies0.0010.005
Scholarly communication0.0050.007
Open science0.0060.008
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0400.006

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 head0.092
GPT teacher head0.458
Teacher spread0.366 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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