Non‐Invasive Diagnosis of Hypertrophic Cardiomyopathy by Breath
Bibliographic record
Abstract
Abstract Undetected in many patients, hypertrophic cardiomyopathy (HCM) is a widespread genetic heart disorder. Conventional diagnosis is based on physiological metrics such as blood pressure, imaging techniques, and genetic testing. Detection of HCM is crucial for proper follow‐up, family screening, early treatment, and risk stratification to prevent sudden cardiac death. Therefore, there is an unmet need for fast and reliable diagnostic methods. This study introduces an innovative approach for the noninvasive, rapid, and accurate diagnosis of HCM by detecting patterns of volatile organic compounds (VOCs) in the patient's breath. Breath from 157 volunteers is collected on sorbent tubes and analyzed using a two‐step approach, gas chromatography‐mass spectrometry (GC‐MS), and a developed nano‐based sensor array. Initially, statistically significant differences in VOC patterns among sampled groups are identified using GC‐MS. Then, the sensor array is used to differentiate between HCM patients and controls, resulting in the testing set, with 92.9% accuracy, 75% specificity, and 94.7% sensitivity. The sensors can further classify subcategories of HCM with >70.3% accuracy for all cases in the testing set. These findings show the applicability of the sensors setup and suggest that VOCs may be a promising noninvasive and real‐time HCM diagnosis option.
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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 | 0.001 | 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".