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Biologic Responders and Super-responders in the International Severe Asthma Registry

2023· article· en· W4367604125 on OpenAlexaff
Eve Denton, Mark Hew, R. Murray, L. Bulathsinhala, T.N. Trung, N. Martin, Mona Al‐Ahmad, Alan Altraja, C.A. Celis-Preciado, Riyad Al‐Lehebi, C. Bergeron, Sinthia Bosnic‐Anticevich, Arnaud Bourdin, Guy Brusselle, Joe Busby, Giorgio Walter Canonica, Jérémy Charriot, George Christoff, Li Ping Chung, Borja G. Cosío, Richard W. Costello, Breda Cushen, Joaquím Fonseca, P.G. Gibson, Liam G. Heaney, Takashi Iwanaga, Magdalene Koh, Lauri Lehtimäki, Jorge Máspero, Bassam Mahboub, Patrick Mitchell, Nikolaos G. Papadopoulos, Diahn-Wang Perng, Matthew Peters, Paul Pfeffer, Todor A. Popov, Celeste Porsbjerg, Chin Kook Rhee, N. Roche, Mohsen Sadatsafavi, S.S. Salvi, Chau‐Chyun Sheu, Carlos A. Torres‐Duque, Charlotte Suppli Ulrik, John W. Upham, E. Wang, M.E. Wechsler, David Price, on behalf of the LUMINANT Working Group

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAsthmaMedicinePoor responderInternal medicine

Abstract

fetched live from OpenAlex

Peer reviewed

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)0.006
metaresearch head score (Gemma)0.036
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 score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.028
GPT teacher head0.297
Teacher spread0.270 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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