Bibliographic record
Abstract
Severe asthma is a complex disease consisting of different endotypes with different inflammatory and clinicopathological characteristics due to the heterogeneity of immune responses and smooth muscle dysfunction. There is an unmet clinical need to develop and to validate biomarkers that can differentiate between the asthma endotypes and guide clinical management, particularly since the availability of biologicals directed against T2 cytokines. The presence of a "Th2 endotype" is currently assessed in clinical practice using markers, such as eosinophil count in sputum or blood, fraction of exhaled nitric oxide, and immunoglobulin E. Individually or in combination, they may help to identify, for example, if the dominant effector protein is interleukin (IL) 5, IL13, or IgE. There is no reliable marker of a "non-Th2 endotype" although sputum neutrophil may provide some indication. The unbiased systems biology approach to severe asthma endotyping which integrates omics signatures and clinical data using large cohort studies may provide more comprehensive information than simple cellular measurements. Novel imaging techniques, such as hyperpolarized noble gas magnetic resonance imaging and computed tomography parametric response maps and metabolomics profiling in breath and other body fluids are also being evaluated as potential biomarkers to guide therapy and to assess prognosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".