The evolving algorithm of biological selection in severe asthma
Bibliographic record
Abstract
New therapeutic options for severe asthma have recently emerged, mostly in the form of monoclonal antibodies ("biologicals") targeting relevant inflammatory pathways. Currently available agents target different aspects of "Type 2" immunity, and their indications often include overlapping patient groups. We present a round-table discussion that took place during the Annual Meeting of the Respiratory Effectiveness Group (REG), on the reasoning behind the use of different add-on medications for severe asthma, and crucially, on selection strategies. The proposed rational is based on current evidence, including real-life studies, as well as on the appreciation of the relevant complexities. Direct head-to-head comparisons of biologicals are lacking; therefore, algorithms for initial choice and potential switch between agents should be based on understanding the key characteristics of different options and the development of a clear plan with predefined targets and shared decision-making, in a structured way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".