Computed Tomography-Based Finite Element Model of the Human Thorax for High-Frequency Chest Compression Therapy
Bibliographic record
Abstract
The computed tomography-based finite element model (CT-FEM) is an increasingly promising tool for the numerical optimization of treatments and therapies.This model enables not just only reducing the number of in vivo studies but also increasing their reproducibility.For that reason, CT-FEM is a critical combination in the design and optimization of treatments and therapies.Highfrequency chest compression (HFCC) therapy with acoustic devices is one of the most promising techniques in terms of providing efficient and independent therapy for airway clearance.However, puzzling operating frequencies in the literature need to be optimized so patients can get the most out of therapy.In this study, a whole human thorax CT image is transformed into 3D realistic chest geometry to illustrate the HFCC effects on the human chest for airway clearance therapy (ACT).The developed CT-FEM consists of soft tissues, rib cage, lungs, scapula, and trachea.It is created using the chest imaging platform with 8121 faces, 12514 edges, and 4236 points.Moreover, the generated comprehensive, realistic, high-quality, simulation model is tested by FEM and supported by different and independent ACT experimental findings from the literature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".