MétaCan
Menu
Back to cohort
Record W3082561169 · doi:10.2147/copd.s263430

<p>A Proposed Approach to Chronic Airway Disease (CAD) Using Therapeutic Goals and Treatable Traits: A Look to the Future</p>

2020· article· en· W3082561169 on OpenAlexaff
Luis Pérez de Llano, Marc Miravitlles, Rafael Golpe, Francisco Javier Álvarez Gutiérrez, Carolina Cisneros, Carlos Almonacid, Eva Martínez-Moragón, Francisco Javier González‐Barcala, David Ramos‐Barbón, Vicente Plaza, José Luís López-Campos, Juan P. de‐Torres, Ciro Casanova, Juan Luis García‐Rivero, Juan Luis Rodríguez Hermosa, Myriam Calle Rubio, Juan José Soler‐Cataluña, Borja G. Cosío

Bibliographic record

VenueInternational Journal of COPD · 2020
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsQueen's University
FundersGrifolsSanofiCSL BehringGlaxoSmithKlineTeva Pharmaceutical IndustriesAstraZeneca
KeywordsMedicineCOPDBronchiectasisAsthmaBiomarkerDiseaseIntensive care medicinePrecision medicineAirway obstructionClinical trialAirwayInternal medicinePathologyLungSurgery

Abstract

fetched live from OpenAlex

Chronic airflow obstruction affects a wide range of airway diseases, the most frequent of which are asthma, COPD, and bronchiectasis; they are clearly identifiable in their extremes, but quite frequently overlap in some of their pathophysiological and clinical characteristics. This has generated the description of new mixed or overlapping disease phenotypes with no clear biological grounds. In this special article, a group of experts provides their perspective and proposes approaching the treatment of chronic airway disease (CAD) through the identification of a series of therapeutic goals (TG) linked to treatable traits (TT) - understood as clinical, physiological, or biological characteristics that are quantifiable using biomarkers. This therapeutic approach needs validating in a clinical trial with the strategy of identification of TG and treatment according to TT for each patient independently of their prior diagnosis.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.449

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)0.0000.000

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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations35
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of COPDSame topicAsthma and respiratory diseasesFrench-language works237,207