Sex differences in clinical risk factors and type-2 biomarkers for predicting asthma attacks in the ORACLE2 patient-level meta-analysis
Bibliographic record
Abstract
Rationale: Multiple risk factors for asthma attacks have been identified, including clinical characteristics, blood eosinophil count (BEC), and exhaled nitric oxide (FeNO). However, the impact of sex on their prognostic value is unclear. Aim: To investigate sex differences in prognostic values of clinical characteristics, BEC and FeNO for severe asthma attacks. Methods: We used the ORACLE2 patient-level meta-analysis of 22 randomized controlled asthma trials’ control arms. Sex-specific characteristics were compared [interquartile range], annualized severe asthma attack rate (ASAAR) ratios were derived from negative binomial models. Results: Among 4,140 women and 2,370 men, ASAAR was higher in women (0.9) than in men (0.74 attacks/patient-year). Women were slightly older (50 [40-59] vs 49 [37-59] years), had higher BMI (28.2 [24.3-33.5] vs 27.6 [24.9-30.9] kg/m2), had more severe asthma (41% GINA step 5 vs 36%), and more comorbid depression/anxiety (15 vs 8%). Prior attacks more strongly predicted future attacks in men (Fig1A). Differences in other risk factors between sexes were minor. BEC and FeNO showed similar prognostic value across sexes, except for a trend towards more asthma attacks in FeNO-low women; and FeNO-/BEC-high men (Fig1B). erj;66/suppl_69/PA5744/F1 F1 F1 Conclusion: The annualized asthma attack rate was higher in women than in men. We found a stronger predictive value of prior attacks in men.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.041 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".