Impact of inotropic support on outcomes in congenital diaphragmatic hernia: A retrospective cohort study
Bibliographic record
Abstract
IntroductionCongenital diaphragmatic hernia (CDH) has complex hemodynamic pathophysiology. There is a paucity of literature to predict outcomes based on the type of medications used for hemodynamic support.MethodsThis is a single-center retrospective cohort. Cases were categorized into different phenotypes: No dysfunction, right ventricle dysfunction, left ventricle dysfunction, and biventricular dysfunction. Medications used for hemodynamic support were categorized into inotropes and vasopressors based on type and dose.StatisticsMean, median, standard deviation, and percentages were used as appropriate. Contingency tables were constructed to compare the distribution of outcomes across different groups. Regression models analyzed the link between hemodynamic phenotype and outcomes.Results69 CDH cases between 2011 and 2023 were analyzed. The mean gestational age at birth was 38.0 weeks (SD 2.4), with a mean birth weight of 3109 g (SD 744 g). The distribution of hemodynamic phenotypes was as follows: No dysfunction phenotype: 43 infants (62.3%), RV phenotype: 7 infants (10.1%), LV phenotype: 7 infants (10.1%), and combined phenotype: 12 infants (17.4%). Inotropes were used in 26 infants (37.7%), vasopressors in 16 infants (23.2%), and a combination of inotropes and vasopressors in 19 infants (27.5%). Outcomes of interest were not different across the different hemodynamic phenotypes. Adjusted logistic regression analysis exploring the impact of LV dysfunction with vasopressor use found higher odds for death (OR = 4.8, p = 0.05).ConclusionInfants with CDH with LV dysfunction and vasopressor exposure are possibly at higher risk for mortality. This is an exploratory finding that warrants further investigation and research to establish the prognosis based on medications used for hemodynamic support.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".