Biomedical applications of wireless continuous wave near infrared spectroscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Progress in research applications of near infrared spectroscopy (NIRS) and growing clinical interest have led to significant improvements in hardware and software. Amongst continuous wave systems the use of light emitting diodes, incorporation of spatially resolved optical geometry, algorithm refinement, development of portable systems, and wireless telemetry led first to portable NIRS instruments, then wearable systems, and now miniaturized self-contained devices. Measurement of absolute tissue oxygen saturation in both muscle and brain, and mapping of event related cortical hemodynamic responses using functional NIRS (fNIRS) have added specific measurement modalities. Wireless wearable systems and self-contained devices capable of measuring such modalities in addition to providing conventional monitoring of trends in oxygenated and deoxygenated haemoglobin concentration from baseline have increased the scope of research, expanded the population readily monitored, and opened new clinical avenues for applications involving NIRS. This review explores the range of biomedical applications reported using wireless continuous wave (CW) NIRS and fNIRS systems, summarizes key elements in the specification of available devices, and outlines potential future directions for clinical use of wireless NIRS technologies.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it