3D Shape Analysis of the Knee Extensor and Flexor Muscles in Patients with COPD using Mesh Projection-based Features
Bibliographic record
Abstract
Abstract. Loss of muscle mass in severe chronic obstructive pulmonary disease (COPD) increases the risk of mortality more than ten-fold compared to those with mild disease. Magnetic resonance imaging (MRI) has been used as a valid, reliable and non-invasive tool to investigate the changes in muscle mass in people with COPD. Using MR images to perform 3D shape analysis of thigh muscles, we extended and applied several state-of-the-art 3D shape descriptors to examine theclassification accuracy of a trained support vector machine classifier to distinguish 4 thigh muscles in 20 people with COPD versus 20 healthy controls. The results of the study show high classification accuracy (with an average of 94%). Such a classification system may identify patients at risk of COPD more readily so that early interventions to reverse muscle atrophy can be provided. 1
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".