Detection of hypoxia by near-infrared spectroscopy and pulse oximetry: a comparative study
Bibliographic record
Abstract
SIGNIFICANCE: Pulse oximetry is widely used in clinical practice to monitor changes in arterial oxygen saturation (SpO2). However, decreases in SpO2 can be delayed relative to the actual clinical event, and near-infrared spectroscopy (NIRS) may detect alterations in oxygenation earlier than pulse oximetry, as shown in previous cerebral oxygenation monitoring studies. AIM: We aim to compare the response of transcutaneous muscle NIRS measures of the tissue saturation index with pulse oximetry SpO2 during hypoxia. APPROACH: Episodes of acute hypoxia were induced in nine anesthetized Yucatan miniature pigs. A standard pulse oximeter was attached to the ear of the animal, and a transcutaneous NIRS sensor was placed on the hind limb muscle. Hypoxia was induced by detaching the ventilator from the animal and reattaching it once the pulse oximeter reported 70% SpO2. RESULTS: Twenty-four episodes of acute hypoxia were analyzed. Upon the start of hypoxia, the transcutaneous NIRS measures changed in 5.3 ± 0.4 s, whereas the pulse oximetry measures changed in 14.9 ± 1.0 s (p < 0.0001). CONCLUSIONS: Transcutaneous muscle NIRS can detect the effects of hypoxia significantly sooner than pulse oximetry in the Yucatan miniature pig. A transcutaneous NIRS sensor may be used as an earlier detector of oxygen saturation changes in the clinical setting than the standard pulse oximeter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".