Fluid handling and blood flow patterns in neonatal respiratory distress syndrome versus transient tachypnea: a pilot study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular and renal adaptation in neonates with Respiratory Distress Syndrome (RDS) and Transient Tachypnea of the Newborn (TTN) may be different. METHODS: Neonates ≥32 weeks were diagnosed with RDS or TTN based on clinical, radiologic and lung sonographic criteria. Weight loss, feeding, urine output, and sodium levels were recorded for the first 3 days, and serial ultrasounds assessed central and organ Doppler blood flow. A linear mixed model was used to compare the two groups. RESULTS: Twenty-one neonates were included, 11 with TTN and 10 with RDS. Those with RDS showed less weight loss (- 2.8 +/- 2.7% versus - 5.6 +/- 3.4%), and less enteral feeds (79.2 vs 116 ml/kg/day) than those with TTN, despite similar fluid prescription. We found no difference in urine output, or serum sodium levels. Doppler parameters for any renal or central parameters were similar. However, Anterior Cerebral Artery maximum velocity was lower (p = 0.03), Superior Mesenteric Artery Resistance Index was higher in RDS, compared to TTN (p = 0.02). CONCLUSION: In cohort of moderately preterm to term neonates, those with RDS retained more fluid and were fed less on day 3 than those with TTN. While there were no renal or central blood flow differences, there were some cerebral and mesenteric perfusion differences which may account for different pathophysiology and management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".