Obstructive Spirometric Index for Asthma Attack Risk Prediction: Analysis from the ORACLE2 Patient-Level Meta-Analysis
Bibliographic record
Abstract
RATIONALE: Low lung function is a key risk factor for asthma attacks. While forced expiratory volume in 1 second (FEV₁) is widely used, it lacks specificity. Incorporating the FEV₁/forced vital capacity (FEV₁/FVC) ratio may better capture obstruction. AIM: To develop an index integrating FEV₁ and FEV₁/FVC to assess asthma attack risk. METHODS: Using ORACLE2, a patient-level meta-analysis of randomised trials’ control arms, we applied a multivariable negative binomial model assessing the combined impact of FEV₁ (% predicted) and FEV₁/FVC (% predicted) on asthma attack risk. The Obstructive Spirometric Index (OSI) was derived from their relative excess risk. OSI, FEV₁, and FEV₁/FVC’s prognostic values were compared across quartiles. RESULTS: Among 5,221 participants (17 trials), both FEV₁ (adjusted rate ratio [aRR] per 10% decrease: 1.05 [1.00–1.09]) and FEV₁/FVC (aRR per 10% decrease: 1.09 [1.03–1.16]) were independent predictors of attack risk. The OSI classification was derived so that the prognostic’s value equaled the aRR for asthma attacks (OSI = 1.15, 15% greater attack risk)(Figure). OSI outperformed FEV₁ and FEV₁/FVC, with a higher aRR across Q2–Q4 vs. Q1 (not shown). CONCLUSIONS: OSI improves asthma attack risk stratification beyond FEV₁ and FEV₁/FVC alone, quantifying risk independently of other clinical and inflammatory parameters. PROSPERO: CRD42021245337; FUNDING: NIHR,FRQS,APQ erj;66/suppl_69/PA1473/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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.043 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".