MEASURING COLLATERAL STATUS IN DIFFERENT VASCULAR BEDS
Bibliographic record
Abstract
INTRODUCTION Collateral circulation in the brain is the most effective predictor of clinical outcome in acute ischemic stroke (AIS) patients [2]. Collaterals are vessels in the brain that reroute blood to the affected tissue during AIS. The only available methods of visualizing these vessels are invasive (CT Angiography, DSA) and are only effective if a major artery is occluded. Faber et al. (2010) showed that a correlation exists between collateral status in various body tissues, with the collateral status of the brain [1]. Here we describe a novel, non-invasive method for determining superficial palmar arch status (conduit collateral status), as well as micro vascular collateral status in the human hand. METHODS The only non-invasive method for determination of the quality of blood flow in the hand is the modified allen’s test (MAT). The current techniques used in this test involve a high level of subjectivity and a low level of accuracy [3]. We improved this test by making the results quantitative, as well as focusing on specific regions of interest (ROI) in the hand. We developed an apparatus for the hand to be placed in, with a mounted research grade camera that would capture the duration of the test. The box was internally illuminated with optimized 740nm LEDs for detection of light intensity changes in the hand during the modified Allen’s test protocol. Our protocol was based upon removing blood from the hand using autonomous compressions, and recording the reperfusion of these vessels from a 1st artery release (radial or ulnar), followed by the 2 nd artery release 15 seconds later. The camera recorded the intensity changes in the reflected radiation off the hand, which fluctuated as haemoglobin exited and re-entered the vessels in the hand. RESULTS Pilot data from 10 hands (5 healthy individuals) showed significant variance in both the rate of filling and the time to return to baseline. Our quickest rate of filling was 24 times larger than the slowest rate. Our analyses of the graphs, as well as controlling various confounding variables during assessment, suggest that these results are physiological and reflect differences in micro-vascular and conduit collateral status. DISCUSSION AND CONCLUSIONS This method reliably measures physiological differences in the collateral status of the human hand. These differences are shown in Figure 1, where two separate subjects have different arterial supply to the digits. In the future, we will be correlating hand collateral status with brain collateral status in stroke patients. If these correlations exist, a non-invasive pre-emptive tool would be made available to gain knowledge of brain collateral status before AIS occurs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".