Lung Clearance Index May Detect Early Peripheral Lung Disease in Sickle Cell Anemia
Bibliographic record
Abstract
RATIONALE: Chronic lung injury is common in sickle cell anemia (SCA) and worsens outcomes. Sensitive lung function tests might predict reversible disease that might benefit from therapeutic interventions. OBJECTIVE: To evaluate whether Lung Clearance Index (LCI), Sacin & Scond, measuring global, intracinar & conductive ventilation inhomogeneity respectively, are more frequently abnormal than lung volumes in young people with SCA. METHODS: Nitrogen multiple breath washout, spirometry and body plethysmography were cross-sectionally evaluated at steady state in subjects with SCA (hemoglobin SS) and healthy controls aged 8-21 years from London, UK. RESULTS: 35 patients (51% boys; mean±SD 16.4±3.5 years) and 31 controls (48% boys; 16.2±3.2 years) were tested. There were significant differences between the study and control group in mean LCI (mean difference 0.42 units, 95%CI 0.22 to 0.63, p = 0.0001), Sacin (mean difference 0.014 units, 95%CI 0.001 to 0.026, p = 0.04), FEV1 (mean difference -0.79 z-scores, 95%CI -1.28 to -0.30, p = 0.002), FVC (mean difference -0.80 z-scores, 95%CI -1.28 to -0.31) and TLC (mean difference -0.79 z-scores, 95%CI -1.25 to -0.29), but not in Scond and FEV1/FVC ratio. While 29% (10/35) of patients had LCI >95th percentile of controls, 23% (8/35) had abnormal FEV1 (<5th of the reference population). CONCLUSION: Lung clearance index detected slightly more abnormalities than lung volumes in young people with SCA. Significant differences with controls in LCI and Sacin but not in Scond and FEV1/FVC ratio suggest that the lung function changes were most likely due to patchy peripheral lung disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".