Development of a hybrid broadband NIRS/diffusion correlation spectroscopy system to monitor preterm brain injury (Conference Presentation)
Bibliographic record
Abstract
In Canada, 8% of births occur prematurely. Preterm infants weighing less than 1500g are at a high risk of neurodevelopmental impairment: 5-10% develop major disabilities such as cerebral palsy and 40-50% show other cognitive and behavioural deficits. The brain is vulnerable to periods of low cerebral blood flow (CBF) that can impair energy metabolism and cause tissue damage. There is, therefore, a need for an efficient neuromonitoring system to alert the neonatal intensive care team to clinically significant changes in CBF and metabolism, before injury occurs. Optical technologies offer safe, non-invasive, and cost-effective methods for neuromonitoring. Cerebral oxygen saturation (ScO2) can be measured by exploiting the absorption properties of hemoglobin though Near-Infrared Spectroscopy (NIRS), and Diffuse Correlation Spectroscopy (DCS) can monitor CBF by tracking red blood cells. These measures can be combined to describe metabolism, a key indicator of tissue viability. In this study we present the development and testing of a hybrid broadband NIRS/DCS neuromonitor. This system is novel in its ability to simultaneously acquire broadband NIRS and DCS signals, providing a truly real-time measure of metabolism. Narrow bandpass and notch filters have been incorporated to diminish light contamination between the two modalities, preferentially filtering out each source from the opposing detector, allowing for an accurate measure of ScO2, CBF, and metabolism. With a broadband NIRS/DCS system, a real-time measure of CBF and metabolism within the developing brain can aid clinicians in monitoring events that precede brain injury, ultimately leading to better clinical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".