Temporal complexity in clinical manifestations of lung disease
Bibliographic record
Abstract
In this review, we summarize results of recent research on the temporal variability of lung function, symptoms, and inflammatory biomarkers. Specifically, we demonstrate how fluctuation analysis borrowed from statistical physics can be used to gain insight into neurorespiratory control and complex chronic dynamic diseases such as asthma viewed as a system of interacting components (e.g., inflammatory, immunological, and mechanical). Fluctuation analysis tools are based on quantifying the distribution and the short- and long-term temporal history of tidal breathing and lung function parameters to assess neurorespiratory control and monitor chronic disease. The latter includes the assessment of severity and disease control, the impact of treatment and environmental triggers, the temporal characterization of disease phenotypes, and the individual risk of exacerbation. While in many cases specific mechanistic insight into the fluctuations still awaits further research, appropriate analyses of the fluctuations already impact on clinical science and practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".