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Record W2978058789 · doi:10.21037/jtd.2019.09.38

Next-generation care pathways for allergic rhinitis and asthma multimorbidity: a model for multimorbid non-communicable diseases—Meeting Report (Part 2)

2019· review· en· W2978058789 on OpenAlexaff
Jean Bousquet, N. Pham‐Thi, Anna Bedbrook, Ioana Agache, Isabella Annesi‐Maesano, Ignacio J. Ansotegui, Josep M. Antó, Claus Bachert, Samuel Benveniste, M. Bewick, Nils Billo, Sinthia Bosnic‐Anticevich, Isabelle Bossé, Guy Brusselle, Moisés A. Calderón, Giorgio Walter Canonica, Luis Caraballo, Victória Cardona, Ana María Carriazo, Eugene Cash, Lorenzo Cecchi, Derek K. Chu, Elaine Colgan, Elı́sio Costa, Álvaro A. Cruz, Stephen R. Durham, Motohiro Ebisawa, Marina Erhola, Jean‐Luc Fauquert, Wytske J. Fokkens, João Fonseca, Nick Guldemond, Tomohisa Iinuma, Maddalena Illario, Ludger Klimek, Piotr Kuna, Violeta Kvedarienė, Désirée Larenas-Linneman, Daniel Laune, Lan Le, Olga Lourenço, João O. Malva, Gert Mariën, Enrica Menditto, Joaquim Mullol, Lars Münter, Yoshitaka Okamoto, Gabrielle L. Onorato, Maritta Perala, Oliver Pfaar, Abigail Phillips, Jim Phillips, Hilary Pinnock, F. Portejoie, Pablo Quinones‐Delgado, C. Rolland, Ulysse Rodts, Bolesław Samoliński, Mario Sánchez‐Borges, Holger J. Schünemann, Mohamed H. Shamji, David Somekh, Alkis Togias, Sanna Toppila‐Salmi, Ioanna Tsiligianni, Omar S. Usmani, Samantha Walker, Dana Wallace, Arūnas Valiulis, Rianne van der Kleij, Maria Teresa Ventura, Siân Williams, Arzu Yorgancıoğlu, Torsten Zuberbier

Bibliographic record

VenueJournal of Thoracic Disease · 2019
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster UniversityImpact
FundersUCB PharmaNovartis PharmaNational Institutes of HealthSanofiTeva Pharmaceutical IndustriesAstraZenecaAllergopharmaMylanAllergy TherapeuticsPfizer
KeywordsMedicineAsthmaHealth careEnvironmental healthAllergyIntensive care medicineImmunologyEconomic growth

Abstract

fetched live from OpenAlex

In all societies, the burden and cost of allergic and chronic respiratory diseases are increasing rapidly. Most economies are struggling to deliver modern health care effectively. There is a need to support the transformation of the health care system into integrated care with organizational health literacy. MASK (Mobile Airways Sentinel NetworK) (1), a new development of the ARIA (Allergic Rhinitis and its Impact on Asthma) initiative (2), and POLLAR (Impact of Air POLLution on Asthma and Rhinitis, EIT Health) (3), in collaboration with professional and patient organizations in the field of allergy and airway diseases, are proposing real-life ICPs—centred around the patient with rhinitis and using mHealth monitoring of environmental exposure

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)0.004
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 score0.013
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0020.004
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 head0.177
GPT teacher head0.403
Teacher spread0.226 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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