Precordial Vibrations Provide Noninvasive Detection of Early-Stage Hemorrhage
Bibliographic record
Abstract
Graded lower-body negative pressure was used to create a hemodynamic response similar to hemorrhage. Echocardiogram measurements showed a maximal reduction of 32.4% in stroke volume. Analysis of systolic time intervals, such as pre-ejection period and left ventricular ejection time (LVET), derived from a seismocardiogram (SCG), were demonstrated to be more sensitive in detection of early-stage hemorrhage compared with pulse pressure, heart rate, and the amplitude features extracted from SCG. In particular, the LVET and pre-ejection period/LVET features, extracted from SCG, were significantly different between, and correlated with, the different stages of lower-body negative pressure (r = 0.9 and 0.88, P < 0.05), for 32 subjects. These results suggest a portable, cost-effective solution for identification of mild or moderate hemorrhage using accelerometers.
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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 | 0.001 |
| 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) | 0.001 | 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".