Compensatory reserve index during central hypovolemia and volume loading in healthy young and older hyperthermic adults: A pilot study
Bibliographic record
Abstract
The compensatory reserve index (CRI), derived from machine learning algorithms from peripherally obtained photoplethysmography signals, provides a non-invasive assessment of cardiovascular stability, that may be useful clinically. Briefly, the CRI device provides a value between 0 and 1, with 1 reflecting full compensable capabilities and 0 reflecting little to no compensable capabilities. However, the CRI algorithm was developed in younger to middle aged adults, such that it is unknown if older age modulates CRI responses to cardiovascular challenges. In young and older subjects, we compared CRI responses to normothermic and hyperthermic progressive lower body negative pressure (LBNP), and volume loading with saline infusion. Eleven younger (20-36 years) and 10 older (61-75 years) healthy participants underwent (1) graded normothermic LBNP up to 30 mmHg, (2) graded hyperthermic (1.5°C increase in blood temperature) LBNP up to 30 mmHg, and (3) infusion of 15 mL/kg saline (volume loading) with hyperthermia maintained. CRI was obtained throughout each procedure. CRI at 30 mmHg LBNP was 0.18 and 0.24 units greater in the older group during normothermic and hyperthermic LBNP, respectively. However, CRI was not different between age groups at any other LBNP stage, nor did CRI change with volume loading regardless of age. In response to passive hyperthermia alone, regression analyses showed that heart rate was the strongest predictor of CRI. Blood temperature, rate pressure product, and stroke volume were also predictive of CRI but to a lesser extent. In conclusion, age attenuates the reduction in CRI during progressive normothermic and hyperthermic LBNP, but only at 30 mmHg. Second, the CRI was unchanged during volume loading in all subjects. Future studies should determine whether the age differences in CRI reflect age differences in LBNP tolerance.
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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) | 0.000 | 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".