Breath-Based Monitoring of High Cholesterol State and Statin Therapy
Bibliographic record
Abstract
Abstract Monitoring the effectiveness of statin therapy in patients with dyslipidemia is essential for ensuring optimal treatment outcomes. The current standard involves lipid profiling via blood tests to detect abnormalities in blood lipids. This study evaluated the feasibility of a non-invasive, breath-based approach to statin therapy monitoring using Noze’s electronic nose (eNose) platform. A total of 35 participants were enrolled, 25 with elevated low-density lipoprotein cholesterol (LDL-C) levels and 10 healthy controls. The high LDL-C group provided breath specimens both before starting statin therapy and after 6 to 8 weeks of treatment. These breath specimens were digitized using Noze’s eNose platform and analyzed using machine learning (ML) algorithms. Results showed a 91% sensitivity and 87% specificity in identifying high blood cholesterol cases, demonstrating the potential of Noze’s eNose platform for non-invasive monitoring of statin therapy through exhaled breath.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".