Near infra-red spectroscopy in a pediatric population undergoing cardiac surgery
Bibliographic record
Abstract
Abstract Background: Intraoperative Near Infra-Red Spectroscopy (NIRS) may reduce postoperative neurologic complications. Use in pediatric populations, NIRS increases, and variations in sensor placement are understudied. Objective: To explore NIRS performance in a pediatric population undergoing cardiac surgery; to describe significant (20%) NIRS deviations from baseline; to correlate events with physiologic variables; to examine the relevance of a second sensor. Methods: Retrospective review of prospectively collected NIRS data. Associations were assessed using Student's t-test, chi-squared test and logistic regression. Results: Significant deviations from baseline were common. Many occurred when unsupported by CPB (cardiopulmonary bypass) or upon CPB initiation. NIRS decreases and increases were significantly associated with PaO2, hematocrit, and MAP (mean arterial pressure) (p<0.05) and paCO2 (p<0.01), respectively. Unilateral deviations were frequent, particularly amongst cyanotic and male patients. Conclusion: In this population, significant NIRS deviations are associated with physiologic variables. A second sensor provided significant information.
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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 | 0.000 | 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) | 0.002 | 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".