Calibration of diffuse correlation spectroscopy with a time-resolved near-infrared technique to yield absolute cerebral blood flow measurements
Bibliographic record
Abstract
A primary focus of neurointensive care is the prevention of secondary brain injury, mainly caused by ischemia. A noninvasive bedside technique for continuous monitoring of cerebral blood flow (CBF) could improve patient management by detecting ischemia before brain injury occurs. A promising technique for this purpose is diffuse correlation spectroscopy (DCS) since it can continuously monitor relative perfusion changes in deep tissue. In this study, DCS was combined with a time-resolved near-infrared technique (TR-NIR) that can directly measure CBF using indocyanine green as a flow tracer. With this combination, the TR-NIR technique can be used to convert DCS data into absolute CBF measurements. The agreement between the two techniques was assessed by concurrent measurements of CBF changes in piglets. A strong correlation between CBF changes measured by TR-NIR and changes in the scaled diffusion coefficient measured by DCS was observed (R(2) = 0.93) with a slope of 1.05 ± 0.06 and an intercept of 6.4 ± 4.3% (mean ± standard error).
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".