Monitoring tissue oxygen availability with near infrared spectroscopy (NIRS) in health and disease
Bibliographic record
Abstract
Near infrared spectroscopy (NIRS) is becoming a widely used research instrument to measure tissue oxygen (O2) status non-invasively. Continuous-wave spectrometers are the most commonly used devices, which provide semi-quantitative changes in oxygenated and deoxygenated hemoglobin in small blood vessels (arterioles, capillaries and venules). Refinement of NIRS hardware and the algorithms used to deconvolute the light absorption signal have improved the resolution and validity of cytochrome oxidase measurements. NIRS has been applied to measure oxygenation in a variety of tissues including muscle, brain and connective tissue, and more recently it has been used in the clinical setting to assess circulatory and metabolic abnormalities. Quantitative measures of blood flow are also possible using NIRS and a light-absorbing tracer, which can be applied to evaluate circulatory responses to exercise along with the assessment of tissue O2 saturation. The venular O2 saturation can be estimated with NIRS by applying venous occlusion and measuring changes in oxygenated vs. total hemoglobin. These various measurements provide the opportunity to evaluate several important metabolic and circulatory patterns in very localized regions of tissue and may be fruitful in the study of occupational syndromes and a variety of diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".