Discriminating Parkinson’s disease patients from healthy controls using nasal respiratory airflow
Bibliographic record
Abstract
Breathing patterns may inform on health. We note that the sites of earliest brain damage in Parkinson’s disease (PD) house the neural pace-makers of respiration. We therefore hypothesized that ongoing long-term temporal dynamics of respiration may be altered in PD. We applied a wearable device that precisely logs nasal airflow over time in 28 PD patients (mostly H&Y stage-II) and 33 matched healthy controls. Each participant wore the device for 24 h of otherwise routine daily living. We observe significantly altered temporal patterns of nasal airflow in PD, where inhalations are longer and less variable than in matched controls (mean PD = −1.22 ± 1.9 (combined respiratory features score), Control = 1.04 ± 2.16, Wilcoxon rank-sum test, z = −4.1, effect size Cliff’s δ = −0.61, 95% confidence interval = −0.79 – (−0.34), P = 4.3 × 10−5). The extent of alteration is such that using only 30 min of recording we detect PD at 87% accuracy (AUC = 0.85, 79% sensitivity (22 of 28), 94% specificity (31 of 33), z = 5.7, p = 3.5 × 10−9), and also predict disease severity (correlation with UPDRS-Total score: r = 0.49; P = 0.008). We conclude that breathing patterns are altered by H&Y stage-II in the disease cascade, and our methods may be further refined in the future to provide an indication with diagnostic and prognostic value. Andelman-Gur et al. use a nasal airflow monitoring device to detect alterations of respiratory dynamics in patients with Parkinson’s Disease. They reveal longer, but less variable, inhalations and show that changes in airflow dynamics are correlated with disease severity, plus 30 min of data is adequate to discriminate patients from controls. In its earliest stages, Parkinson’s disease damages the parts of the brain that control breathing. We built a small device that measures airflow patterns through the nose over time. People with Parkinson’s disease and healthy individuals wore this device for 24 h. We found that nasal inhalations in Parkinson’s patients were longer and less variable than in healthy individuals. This difference was so pronounced that, using only 30 min of recording, we could accurately determine most people who had Parkinson’s disease and how severe their disease was. Future studies will determine whether this tool can contribute to early diagnosis, and it may be useful to monitor disease progression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".