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Record W2072167136 · doi:10.4103/1817-1737.94519

Personalized medicine for asthma: Are we there yet?

2012· article· en· W2072167136 on OpenAlex
AudreyH Poon, Qutayba Hamid

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

VenueAnnals of Thoracic Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineAsthmaWheezeNatural historyDiseasePopulationEosinophilicImmunologyIntensive care medicinePediatricsInternal medicinePathology

Abstract

fetched live from OpenAlex

Asthma has generally been defined as a chronic disorder of the lung, with variable airway obstruction, wheeze/cough, and an underlying inflammatory process. However, considerable heterogeneity exists within the population of patients with asthma-like symptoms. Clinically, asthma is being categorized into eosinophilic, neutrophilic, atopic, non-atopic, early onset, and late onset; and aspirin- or exercise-induced in cases with known triggers. [1] The disease is currently recognized as a complex condition with variable severity, natural history, and response to treatment. Treating asthma based on phenotypes that group observable characteristics, with no direct relationship to the disease mechanisms is suboptimal, given the variability in treatment response. Endotyping, which refers to defining subpopulations of a disease based on molecular mechanisms or treatment response has shown some success in designing a more effective treatment scheme.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.153
GPT teacher head0.447
Teacher spread0.294 · 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