<title>Comparison of near-infrared spectroscopy with CT cerebral blood flow measurements in newborn piglets</title>
Bibliographic record
Abstract
Severely premature infants are often at high risk of cerebral hemorrhage or ischemic injury due to their inability to properly regulate blood flow to the brain. If blood flow is too high, the infant is at risk of cerebral hemorrhage, while too little blood flow can result in ischemic injury. The purpose of this research is to design and develop a means of non-invasively measuring cerebral blood flow (CBF) with near infrared spectroscopy (NIRS). Such a device would greatly aid the diagnosis and monitoring of afflicted infants. Previous attempts to measure CBF with NIRS have achieved limited success. In this study we acquired high signal-to-noise NIR spectrum from 600 to 980 nm with a cooled CCD spectrometer. This spectrometer enables the differential path length factor (DPF) to be estimated with accuracy using a second derivative technique described by Matcher et al. The validity of our new approach is determined via direct comparison with a previously validated computed tomography (CT) method. Three newborn piglets were studied. CBF measurements were performed at various partial arterial CO2 tensions (PaCO2) using both the NIRS and CT methods. The results of the two methods correlate well with a relationship of CBFCT equals -4.30 + 1.05 CBFNIRS (r2 equals 0.96).
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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 | 0.000 |
| 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".