Depth-enhanced time-resolved NIRS monitoring of deep brain regions at risk of injury in preterm infants
Bibliographic record
Abstract
Intraventricular hemorrhage (IVH) is a health risk faced by preterm infants, with the potential for severe health consequences. Near-infrared spectroscopy (NIRS) can be used to investigate hemodynamics in the brain, however its limited depth sensitivity means it is not as sensitive to the deeper white matter surrounding the lateral ventricles, where IVH occurs. For this reason, developing an optical system with improved depth sensitivity may prove beneficial for using NIRS to probe IVH in preterm neonates. Gated detectors are able to make use of higher laser power by keeping the detector closed until after the initial strong peak, thereby avoiding oversaturation while increasing sensitivity to late photons. The objective of this work was to compare the depth sensitivity of using the gated and non-gated modes of the same detector, to determine its efficacy for this application. A fast-gated detector was tested using a tissue-mimicking phantom, with an inclusion that was moved progressively deeper into the phantom and away from the source and detector (in 1-cm increments, from 1 to 3-cm). A time-correlated single-photon counting unit was used to record the arrival times of the photons to build a distribution of time of flight (DTOF). This DTOF was then compared to the gated measurement, for which the gate was delayed opening until the last 10% of the DTOF. When comparing the late period of the DTOF that corresponds to the same timing as the gate, the change in signal was more prominent for the gated mode than for the signals extracted from the DTOF. While isolating the late period of the DTOF improved depth sensitivity compared to the total number of photons, it was limited by the increase in noise; using a gated detector resulted in better depth sensitivity and less variability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".