Lung Clearance Index May Detect Early Peripheral Lung Disease in Sickle Cell Anemia
Bibliographic record
Abstract
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. Objectives To evaluate whether lung clearance index (LCI) (measuring global ventilation inhomogeneity), intraacinar ventilation inhomogeneity (Sacin), and conductive ventilation inhomogeneity (Scond) 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 control subjects aged 8–21 years from London, United Kingdom. Results Thirty-five patients (51% boys, mean ± SD age, 16.4 ± 3.5 yr) and 31 control subjects (48% boys; 16.2 ± 3.2 yr) were tested. There were significant differences between the study and control groups in mean LCI (mean difference, 0.42 units; 95% confidence interval [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), forced expiratory volume in 1 second (FEV1) (mean difference, −0.79 z-scores; 95% CI, −1.28 to −0.30; P = 0.002), forced vital capacity (FVC) (mean difference, −0.80 z-scores; 95% CI, −1.28 to −0.31, P = 0.002), and total lung capacity (mean difference, −0.76 z-scores; 95% CI, −1.25 to −0.29, P = 0.002), but not in Scond and FEV1-to-FVC ratio. Whereas 29% (10 of 35) of patients had LCI > 95th percentile of control subjects, 23% (8 of 35) had abnormal FEV1 (<5th percentile of the reference population). Conclusions LCI detected slightly more abnormalities than lung volumes in young people with SCA. Significant differences from control subjects in LCI and Sacin but not in Scond and FEV1-to-FVC ratio suggest that the lung function changes were most likely owing to patchy peripheral lung disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".