Model study of inspiratory fall of blood pressure in airway obstruction
Bibliographic record
Abstract
Abstract: Inspiratory fall of systolic blood pressure (IFSBP) is used as an index to assess the respiratory influences on non-linear dynamics of heart rate variability. In an in-vivo situation it is particularly difficult to isolate individual effects of heart rate, vascular tone, pleural pressure variation, and ventricular interdependence. A computer model of the cardiopulmonary system was adapted to this problem, relating mechanisms such as baroreflex regulation of heart rate in response to respiratory oscillations. The model provided time-course simulations of hemodynamics by numerically integrating 28 nonlinear, time-varying differential equations. Two approaches for baroreflex regulation were tested, including a simple 1st-order relationship between R-R interval (RRI) and SBP and the autoregressive moving average (ARMA) model. Experimental data were obtained retrospectively from 22 patients with chronic airway obstruction before and during breathing through an external resistance. Magnitude and phase relations between arterial pressure and pleural pressure were evaluated. The computer model provided good fits to arterial pressure waveforms: correlation coefficients (r) ranging from 0.71 to 0.96 (mean±SD: 0.87±0.06) with a simple 1st-order RRI-SBP model. It was observed that the ARMA model did not further improve the goodness of fit. However, no dominant parameter was found for phase relations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".