Multivariable prognostic relations of asthma attack risk factors in the ORACLE patient-level meta-analysis
Bibliographic record
Abstract
Rationale: Risk factors for severe asthma attacks include asthma treatment step, attack history, low lung function, uncontrolled symptoms and type-2 biomarkers blood eosinophil count (BEC) and exhaled nitric oxide (FeNO). However, multivariable prognostic relations remain unclear. Aim: To assess the prognostic relationships of baseline risk factors with future severe asthma attacks (defined as ≥3 days systemic steroids). Methods: We included 6516 participants from control arms of 22 randomised controlled trials (6-12m follow-up, the OxfoRd Asthma attaCk risk scaLE (ORACLE) patient-level meta-analysis). Rate ratios for the annualised asthma attack rate were derived from 2 negative binomial models: 1) univariable, 2) adjusted for treatment step, attack in past 12m, Asthma Control Questionnaire-5 (ACQ5), FEV1%, log10BEC and log10FeNO. Results: Rate ratios for severe asthma attacks in the multivariable model were treatment step (1 vs 3), 0.13 [95%CI in Fig]; attack in past 12m (yes/no), 1.94; FEV1% (per 10% decrease) 1.11; ACQ5 (per 0.5 increase) 1.10 ; log10BEC 1.32; and log10FeNO, 1.49 (Fig). Conclusion: Asthma treatment step, attack history, lower FEV1%, ACQ5, and type-2 biomarkers BEC and FeNO are important risk factors for asthma attacks. Further ORACLE analyses should aim for a prognostic model to support clinical decision-making. Prospero: CRD42021245337 Funding: NIHR,QRHRN,FRQS,APQ,SAB,LUF erj;64/suppl_68/OA3761/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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.039 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".