Type-2 biomarker elevation across asthma treatment steps in the ORACLE patient-level meta-analysis of all-comer trials
Bibliographic record
Abstract
Rationale: The relationship between asthma treatment step and prevalence of raised blood eosinophil counts (BEC) and exhaled nitric oxide (FeNO) is unclear. Aim: To assess the proportion of trial patients with raised baseline BEC and FeNO across asthma treatment steps. Methods: We included participants from the control arms of 13 randomised controlled asthma trials not enriching for biomarkers (Novel START, PRACTICAL, CAPTAIN, Dupilumab phase 2b, LAVOLTA 1-2, LUTE-VERSE, MILLY, PACT, QUEST, STRATOS 1-2; all part of the OxfoRd Asthma attaCk risk scaLE (ORACLE) patient-level meta-analysis). Proportions of patients were tabulated for baseline BEC×FeNO categories per treatment step. A proportion test assessed differences in biomarker prevalence across steps in 10 imputed datasets. Results: 4292 patients were included. Raised biomarkers occurred significantly more in mild asthma (Fig). Baseline BEC and FeNO elevation (≥0.3×109/L and ≥50 ppb) was observed in n=61/315 (27%) of step 1, 63/315 (20%) of step 2, 50/846 (6%) of step 3, 175/1635 (11%) of step 4, and 158/1271 (12%) of step 5 populations (p<2.2E-16). Conclusion: In unselected clinical trial populations, BEC and FENO elevation is seen more often in less intensely treated asthma. These results highlight the complexity between type-2 biomarker status, treatment, and clinical impact. Prospero: CRD42021245337; Funding: NIHR, QRHRN, FRQS, APQ, SAB, LUF erj;64/suppl_68/PA1210/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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.042 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".