Late Breaking Abstract - Up-dosing of reslizumab in severe asthmatics with persistent sputum eosinophilia
Bibliographic record
Abstract
Rationale: A subset of severe eosinophilic asthmatics (SEA) remains uncontrolled despite anti-IL-5 biologic which may be due to inadequate neutralisation of airway IL-5. Aim: To investigate whether sequential up-dosing of a weight-adjusted anti-IL-5 mAb (reslizumab) will decrease sputum eosinophils(sp-eos) and improve asthma control. Method: We conducted a prospective, single-centre, open-label, 52-weeks dose-escalation (DE) study ( NCT04710134 ). SEA with sp-eos>3% after 16 weeks of standard dose (3mg/kg, iv, Q4W) underwent DE to 4 mg/kg after V6 (4 infusions) or 5 mg/kg after V10 (4 infusions). Sp-eos, cytokines, anti-eosinophil peroxidase (EPX) IgG, FEV1, and ACQ-5 were examined. Results: Ten SEA (53±16 yrs; 6F) started reslizumab at 3 mg/kg (V1). After 4 infusions (V6), 4 (40%) had sp-eos>3% and ACQ-5<1.5, while n=5 (sp-eos 16±11%) were DE to 4 mg/kg, and one to 5 mg/kg. Reslizumab reduced sp-eos, EPX, IL-5 and improved FEV1 at V14 (p<0.05), irrespective of dosing. Despite reduced sp-IL-5 after DE, 2/10 patients (20%) had sp-eos at V14 (>3%, Fig 1A). Reslizumab did not reduce sp-IL-13 or anti-EPX levels. At V14, anti-EPX IgG were elevated in 5/6 DE patients and significantly correlated with IL-13 (r=0.7; P=0.02, Fig 1D). Conclusion: A proportion of SEA may benefit from higher doses of anti-IL-5 mAb that allow normalisation of sputum eosinophils and IL-5. A subset remains symptomatic with evidence of sp-eos, IL-13, and anti-EPX IgG. erj;64/suppl_68/PA3943/F1 F1 F1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".