Improved Cardiac Performance with Dexamethasone Therapy in Premature Neonates: Novel Insights Using Serial Echocardiographic Assessments
Bibliographic record
Abstract
(1) Background: dexamethasone is used for the prevention and treatment of chronic lung disease (CLD) in premature neonates, and its impact on cardiac performance and pulmonary vascular resistance has not been well studied. (2) Methods: eligible neonates of <30 weeks gestational age (GA) had echocardiograms performed on them at three time points—before the initiation of dexamethasone (Echo-1), 24–48 h post the completion of dexamethasone therapy (Echo-2), and 7–14 days after course completion (Echo-3). (3) Results: 28 neonates with a 25.2 week mean GA and 652.9 g birthweight were included. The mean cumulative dose of dexamethasone was 0.98 mg/kg, given over 8–10 days. Echo-1 and Echo-2 showed a significant improvement in the right ventricular fractional area change (RV FAC 44.88 vs. 49.71, p = 0.025), tricuspid annular plane systolic excursion (TAPSE 0.65 cm vs. 0.70 cm, p = 0.013), and RV S’ (7.18 vs. 8.56, p = 0.05). The left ventricular (LV) ejection fraction was similar but with a significant increase in the LV S’ (4.77 vs. 6.01, p = 0.006). A longitudinal analysis at three time points showed a significant increase in RV FAC (0.02 units 95% CI (0.00–0.04), p = 0.037), TAPSE (0.09 units 95% CI (0.06–0.13), p < 0.001), RV S’ (0.97 units (95% CI = 0.11–1.84), p = 0.028), a reduction in the eccentricity index (0.07 units 95% CI (−0.14–−0.01), p = 0.030), and an increase in the LV S’ (0.56 units (95% CI = 0.18–0.94)). (4) Conclusion: The use of postnatal dexamethasone for the prevention/treatment of CLD in premature neonates resulted in an expected improvement in respiratory status along with a significant improvement in the echocardiographic measures of biventricular heart performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".