Does regional tissue oxygenation vary in infants transported by road and air. A prospective observational within-subject analysis of regional oxygenation measured by near infrared spectroscopy in infants transported in Western Australia.
Bibliographic record
Abstract
Objective Infants in Western Australia who are unwell frequently require air retrieval for tertiary care. Travelling at altitude may worsen hypoxia. Design, Setting and Patients We examined tissue oxygenation using near infrared spectroscopy (NIRS) during retrieval by road and air. Study design was prospective and observational comparing cerebral/mesenteric oxygenation (crS02 and mS02) and fractional tissue extraction (cFTOE and mFTOE) in newborns transported by fixed-wing aircraft and road ambulance. Transport by fixed-wing aircraft was a combination of by road and by air. Transport by ambulance was by road only. Primary outcome measure was a within-subjects comparison of oxygenation and tissue oxygen extraction (flight vs. road transit). Measurements in those transported by road were made as control (first and third quarter of road transit). Results There were 24 infants transported by air and 31 by road. The median (interquartile range) gestations and weights were similar [39+1(31 to 41+3) vs. 40+1(35+3 to 43) wk; 3.3 (1.8 to 4.95) vs. 3.2 (2.2 to 4.2) Kg]. Most were treated for respiratory disease. There was no difference in SpO2, respiratory support or haemoglobin between groups. During transport by fixed-wing aircraft, infants had lower crS02 (75.3 (6.2) vs 77.9 (6.2); p=<0.0001) and mS02 (69.3 (16.6) vs. 74.2 (11.6); p=<0.01) and increased cFTOE (0.18 (0.07) vs. 0.21 (0.07), p<0.001) and mFTOE (0.22 (0.12) vs. 0.28 (0.17); P=0.006) than when travelling by road. There was no difference in the control group. Conclusion Newborn infants who travelled by air had lower cerebral saturations and greater oxygen tissue extraction when travelling by air. The pathogenesis and impact of these findings need further exploration.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".