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Record W3029612125 · doi:10.1111/all.14425

EAACI Biologicals Guidelines—Recommendations for severe asthma

2020· article· en· W3029612125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAllergy · 2020
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of TorontoSickKids FoundationSt. Joseph’s Healthcare HamiltonHospital for Sick ChildrenMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineAsthmaIntensive care medicineDisease managementHealth careRegimenHealthcare systemAlternative medicineHealth management systemImmunologyPathology

Abstract

fetched live from OpenAlex

Severe asthma imposes a significant burden on patients, families and healthcare systems. Management is difficult, due to disease heterogeneity, co-morbidities, complexity in care pathways and differences between national or regional healthcare systems. Better understanding of the mechanisms has enabled a stratified approach to the management of severe asthma, supporting the use of targeted treatments with biologicals. However, there are still many issues that require further clarification. These include selection of a certain biological (as they all target overlapping disease phenotypes), the definition of response, strategies to enhance the responder rate, the duration of treatment and its regimen (in the clinic or home-based) and its cost-effectiveness. The EAACI Guidelines on the use of biologicals in severe asthma follow the GRADE approach in formulating recommendations for each biological and each outcome. In addition, a management algorithm for the use of biologicals in the clinic is proposed, together with future approaches and research priorities.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.111
GPT teacher head0.361
Teacher spread0.250 · 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