Measuring Tissue Oxygen Saturation via Spectrometer in Children
Bibliographic record
Abstract
BACKGROUND: : Near-infrared spectroscopy is a new noninvasive method of monitoring oxygen saturation at a tissue level. The purpose of this study was to evaluate new near-infrared tissue spectrometer InSpectra (Hutchinson Technology Inc.) in children and to determine preferable areas of the body to measure tissue oxygen saturation (StO2). METHODS: : Prospective study at a pediatric emergency department. Children aged 0 years to 17 years with no respiratory distress participated in this study. StO2 on deltoid muscle, thenar eminence, forearm, calf, bicep, and tricep was measured at triage with a 25-mm probe. RESULTS: : A total of 310 patients were recruited. The mean age of participants was 6.8 years +/- 4.4 years and 53% were males. Average StO2 was 84% (95% confidence interval, 81-87%) on the bicep muscle, 83.5% (95% confidence interval, 82-85%) on the deltoid muscle and significantly (p < 0.05) lower on other areas. Variation of StO2 was lower on the bicep, deltoid, and thenar muscles. Regression analysis showed significant linear relationship between patients' age and StO2 measured on the thenar eminence (beta = 0.3, R = 0.08, p < 0.001) and between patients' weight and StO2 on the thenar eminence (beta = 0.3, R = 0.07, p < 0.001). StO2 in febrile patients was similar to afebrile children, except thenar eminence where StO2 was significantly lower (p = 0.002). Less than 5% reported any type of pain or cried during StO2 measurement, which did not differ from pulse oximetry. CONCLUSION: : Bicep and deltoid muscles are the most appropriate areas to measure StO2 using the 25-mm transducer in children of different ages. The use of near-infrared spectroscopy on the thenar eminence, which is usually used for measurement in adults, has varied results in children depending on the age, weight, and presence of fever.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".