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Record W4205763276 · doi:10.1016/j.jaip.2021.12.027

Global Variability in Administrative Approval Prescription Criteria for Biologic Therapy in Severe Asthma

2022· review· en· W4205763276 on OpenAlexaff
Celeste Porsbjerg, Andrew Menzies‐Gow, Trung N. Tran, Ruth Murray, Bindhu Unni, Shi Ling Audrey Ang, Marianna Alacqua, Mona Al‐Ahmad, Riyad Al‐Lehebi, Alan Altraja, А. S. Belevskiy, Unnur Steina Björnsdóttir, Arnaud Bourdin, John Busby, Giorgio Walter Canonica, George Christoff, Borja G. Cosío, Richard W. Costello, J. Mark FitzGerald, João Fonseca, Susanne Hansen, Liam G. Heaney, Enrico Heffler, Mark Hew, Takashi Iwanaga, D.J. Jackson, Janwillem Kocks, Maria Kallieri, Hsin-Kuo Bruce Ko, Mariko Siyue Koh, Désirée Larenas‐Linnemann, Lauri Lehtimäki, Stelios Loukides, Njira Lugogo, Jorge Máspero, Andriana Ι. Papaioannou, Luis Pérez de Llano, Paulo Márcio Pitrez, Todor A. Popov, Linda Makowska Rasmussen, Chin Kook Rhee, Mohsen Sadatsafavi, Johannes Martin Schmid, Salman Siddiqui, Camille Taillé, Christian Taube, Carlos A. Torres‐Duque, Charlotte Suppli Ulrik, John W. Upham, Eileen Wang, Michael E. Wechsler, Lakmini Bulathsinhala, Victoria Carter, Isha Chaudhry, Neva Eleangovan, Naeimeh Hosseini, Mari-Anne Rowlands, David Price, Job F. M. van Boven

Bibliographic record

VenueThe Journal of Allergy and Clinical Immunology In Practice · 2022
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilSeqirusGenentechGrifolsAstellas PharmaAstraZenecaChiesi FarmaceuticiUK Research and InnovationCovis PharmaHandokAarhus UniversitetSanofiTeijin PharmaDaiichi-SankyoTeva Pharmaceutical IndustriesGlaxoSmithKlineSingapore General HospitalAmgen
KeywordsMedicineAsthmaMedical prescriptionMEDLINEDrug approvalIntensive care medicineFamily medicineInternal medicinePharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Regulatory bodies have approved five biologics for severe asthma. However, regional differences in accessibility may limit the global potential for personalized medicine. OBJECTIVE: To compare global differences in ease of access to biologics. METHODS: In April 2021, national prescription criteria for omalizumab, mepolizumab, reslizumab, benralizumab, and dupilumab were reviewed by severe asthma experts collaborating in the International Severe Asthma Registry. Outcomes (per country, per biologic) were (1) country-specific prescription criteria and (2) development of the Biologic Accessibility Score (BACS). The BACS composite score incorporates 10 prescription criteria, each with a maximum score of 10 points. Referenced to European Medicines Agency marketing authorization specifications, a higher score reflects easier access. RESULTS: Biologic prescription criteria differed substantially across 28 countries from five continents. Blood eosinophil count thresholds (usually ≥300 cells/μL) and exacerbations were key requirements for anti-IgE/anti-IL-5/5R prescriptions in around 80% of licensed countries. Most countries (40% for dupilumab to 54% for mepolizumab) require two or more moderate or severe exacerbations, whereas numbers ranged from none to four. Moreover, 0% (for reslizumab) to 21% (for omalizumab) of countries required long-term oral corticosteroid use. The BACS highlighted marked between-country differences in ease of access. For omalizumab, mepolizumab, benralizumab, and dupilumab, only two, one, four, and seven countries, respectively, scored equal or higher than the European Medicines Agency reference BACS. For reslizumab, all countries scored lower. CONCLUSIONS: Although some differences were expected in country-specific biologic prescription criteria and ease of access, the substantial differences found in the current study present a challenge to implementing precision medicine across the world.

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)0.007
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.136
GPT teacher head0.471
Teacher spread0.335 · 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
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

Citations71
Published2022
Admission routes1
Has abstractno

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