Near Infrared Spectroscopy (NIRS) in the clinical setting – An adjunct to monitoring during diagnosis and treatment
Bibliographic record
Abstract
Since clinical near infrared spectrophotometers (NIRS) first became commercially available in the early 1990′s there have been more than two thousand related peer reviewed reports in the medical literature. These encompass a wide range of human and animal trials that have been directed at validating the principles, methods, and algorithms underlying the technology, as well as demonstrating its potential for specific clinical uses such as detecting concealed bleeding, onset of hypoxia, progress of ischemia, and tissue oxygenation status within human brain, muscles, organs, and tumours. In addition to its standard use as a monitor of patterns of change in the concentrations of oxygenated, and de‐oxygenated hemoglobin residing in blood, NIRS has also been used to monitor patterns of change in the redox status of the cellular respiration enzyme, cytochrome c oxidase (Cyt a,a3) which utilizes the oxygen diffused from the blood. Accompanied by a tracer bolus of near infrared absorbing dye, NIRS has also been used to measure the proportional blood flow and blood volume transiting organs. NIRS has been used in conjunction with PET, fMRI, BOLD‐fMRI, TCD, vascular flowmetry, MRS, NMR, plethysmography, PO2 histography, EEG, ECG, EMG, SSEP, MEP, MEG, and standard bedside monitoring devices. Herein we summarize the history, technique, algorithms, methods and advances of clinical NIRS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".