Is renal tissue oxygen desaturation during severe hypoxia underestimated? An observational study in term newborn piglets
Bibliographic record
Abstract
The kidney is an organ highly susceptible to injury by regional tissue oxygen desaturation during hypoxic episodes. Transcutaneous monitoring of renal tissue oxygen saturation is therefore of increasing interest. The aim of the present study was to compare renal tissue oxygen saturation measured by near-infrared spectroscopy (NIRS) during acute hypoxia in neonates directly on the kidney and transcutaneously. We hypothesized that transcutaneous renal tissue oxygen saturation measurements would be influenced by superficial tissue. Five term newborn piglets were anesthetized, instrumented and exposed to normocapnic hypoxia at an inspired oxygen concentration of 0.14. Regional tissue oxygen saturation (rSO₂) was simultaneously measured for comparison with the sensor of NIRS (Invos 5100, Somanetics Corp., Troy, MI, USA) applied directly on the left kidney (renaldirect rSO₂) and on the skin of right flank above the right kidney (renalskin rSO₂). Cerebral regional tissue oxygenation (cerebralskin rSO₂), arterial oxygen saturation, heart rate (HR) and mean arterial pressure (MAP) were also monitored. NIRS parameters were analyzed in 5 s intervals during first 2 min of hypoxia. Hypoxia was achieved with an arterial oxygen desaturation from median (range) 95.3% (86.8-98.0) to 23.5% (13.0-41.0) after 2 min. HR and MAP did not change significantly during hypoxia. There were pronounced and lower renaldirect rSO₂ readings when compared with those of renalskin rSO₂ with significant differences from 25 to 55 s after initiation of hypoxia. Changes of cerebralskin rSO₂ and renalskin rSO₂ were similar. Transcutaneous monitoring of renal tissue oxygen saturation may underestimate acute oxygen desaturation of the kidney during hypoxia in neonates.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".