A parametric model of the relationship between EIT and total lung volume
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Bibliographic record
Abstract
Spirometry and electrical impedance tomography (EIT) data from 26 healthy subjects (14 males, 12 females) were used to develop a model linking contrast variations in EIT difference images to lung volume changes. Eight recordings, each 64 s long, were made for each subject in four postures (standing, sitting, reclining at 45 degrees, supine) and two breathing modes (quiet tidal and deep breathing). Age, gender and five anthropometric variables were recorded. The database was divided into four subsets. The first subset, data from 22 subjects (12 males, 10 females) recorded in deep breathing mode, was used to create the model. Validation was done with the other subsets: data recorded during quiet tidal breathing in the same 22 subjects, and data recorded in both breathing modes for the other four subjects. A quadratic equation in DeltaV(P) (lung volume changes recorded by the spirometer) provided a very good fit to total contrast changes in the EIT images. The model coefficients were found to depend on posture, gender, thoracic circumference and scapular skin fold. To validate the model, the quadratic equation was inverted to estimate lung volume changes from the EIT images. The estimated changes were then compared to the measured volume changes. Validations with each data subset yielded mean standard errors ranging from 9.3% to 12.4%. The proposed model is a first step in enabling inter individual comparisons of EIT images since: (1) it provides a framework for incorporating the effects of anthropometric variables, gender and posture, and (2) it references the images to a physical quantity (volume) verifiable by spirometry.
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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 | 0.000 |
| 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) | 0.000 | 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 it