Physiological influence of basic perturbations assessed by non-invasive optical techniques in humans
Bibliographic record
Abstract
New non-invasive techniques enabling frequent or continuous assessments of various pathophysiological conditions might be used to improve in-hospital outcome by enabling earlier and more reliable bedside detection of medical deterioration. In this preclinical study, three modern non-invasive optical techniques, laser Doppler imaging (LDI), near-infrared spectroscopy (NIRS), and tissue viability imaging (TVI), were all evaluated with respect to the influence of basic physiological perturbations (including local changes in arm positioning, skin temperature, and regional blood flow conditions) on quasi simultaneously obtained values of skin perfusion, muscle tissue oxygenation (StO₂), and skin blood volume, recorded in eighteen healthy volunteers. Skin perfusion measured by LDI responded prominently to changes in positioning of the arm, whereas muscle StO₂ measured by NIRS did not change significantly. Total haemoglobin count (HbT) measured by NIRS and blood volume estimated by TVI both increased significantly on lowering of the limb. On local cooling, the perfusion and blood volume were both found to increase considerably, while StO₂ and HbT did not change. Local heating induced a more than 10-fold increase in skin perfusion and a small increase in blood volume. On progressive venoarterial occlusion, the perfusion, StO₂, HbT, and blood volume values decreased, after transient increases in HbT and blood volume before full arterial occlusion occurred, and all values approached the baseline level on release of the occlusion with a slight overshoot of the StO₂. The results obtained have potential bearing on future utilization of these non-invasive techniques in the management of severely injured and (or) critically ill patients.
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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 | 0.001 | 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 | 0.001 |
| 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".