Thoracic Electric Impedance Tomography Detects Lung Volume Changes in Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
ABSTRACT Introduction/Aims Spirometry is the conventional means to measure lung function in amyotrophic lateral sclerosis (ALS), but is dependent on patient effort and bulbar strength. We aimed to use electric impedance tomography (EIT), an emerging non‐invasive imaging modality, to measure dynamic lung volume changes. Methods Twenty‐one patients with ALS underwent sitting and supine spirometry for forced vital capacity (FVC), and sitting and supine EIT. There were 13 patients in the high FVC group (FVC ≥ 80% predicted) and 8 in the low FVC group (FVC < 80% predicted). Additional demographic and clinical data were collected from clinical records. Results Only the low FVC group had significant loss of lung volumes in the supine position (R 2 = 0.89 and p < 0.001). The supine volume loss measurement at 10 min correlated with sitting (r 2 = 0.47) and supine FVC (r 2 = 0.36), maximum inspiratory (r 2 = −0.44) and expiratory pressures (r 2 = 0.36) (MIP and MEP), and the ALS Functional Rating Scale‐Revised (ALSFRS‐R) dyspnea subscore (r 2 = 0.36). Discussion EIT is an emerging alternative to existing measures of lung function in ALS, but without need for patient effort or bulbar strength. Significant losses in lung volume are seen on supine compared to upright position in patients with respiratory dysfunction. Further study is needed to determine relationships to existing clinical measures.
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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 | 0.000 | 0.001 |
| 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".