Comparison of the SenSmart™ and the INVOS™ neonatal cerebral near-infrared spectrometry devices
Bibliographic record
Abstract
Objectives To determine the correlation and agreement between the SenSmart™ and the INVOS™ devices of neonatal cerebral regional oxygen saturation (CrSO2) measurements using neonatal sensors. The secondary objective was to develop a regression model that predicts CrSO2-INVOS values using CrSO2-SenSmart indices and determine whether the values between the devices are interchangeable. Methods A prospective, cross-sectional study was conducted in infants during the first 4 weeks of life. Simultaneous, bilateral CrSO2 was measured using the SenSmart™X100 (CrSO2-SenSmart) or INVOS™ 5100C (CrSO2-INVOS) device in each frontoparietal area for 2 h. Five-minute CrSO2 values were extracted for analysis. Results Thirty infants were recruited with 720 pairwise measurements and 26 (84%) were evaluated in the first week of life. Mean gestational age of the preterm and term infants was [30.9 ± 2.8 (n = 14) and 38.8 ± 1.1 (n = 16)] weeks, respectively. Overall CrSO2- was 77.08 ± 9.70% and 71.45 ± 12.74% for the SenSmart and INVOS, respectively (p < 0.001). The correlation coefficient (r) between the CrSO2-SenSmart and INVOS was 0.20 (p < 0.001). The mean difference between the CrSO2-SenSmart and INVOS was 5.63 ± 13.87% with −21.6% to 32.8% limits of agreement. The r and mean difference was 0.39 (p < 0.001) and 8.87 ± 12.58% in preterm infants, and 0.06 (p = 0.27) and 2.79 ± 14.34 in term infants. Conclusion The CrSO2-SenSmart tended to read higher than the CrSO2-INVOS device. There was no correlation between the CrSO2-SenSmart and the CrSO2-INVOS in term infants and it was weak in preterms. Due to imprecise agreement, the CrSO2-SenSmart values are not interchangeable with those of the CrSO2-INVOS.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".