Intraventricular hemorrhage: morbidity and risk factors in 3rd level centers in South-East Poland
Bibliographic record
Abstract
Introduction We describe the incidence of intraventricular hemorrhage (IVH) in preterm infants in all 3rd level centers in Podkarpackie province, Poland. We identify the frequency of risk factors present in this population known to increase the occurrence of IVH, which if changed could result in better treatment outcomes. Material and methods The retrospective, observational, multicenter study included 340 preterm infants who were born at ≤ 28 weeks of gestation. Patients hospitalized at 3rd level centers between 2016 and 2020 were enrolled in the analysis. Results The incidence of IVH in the study population was 51%, and severe grades of IVH (sIVH) occurred in 24% of all infants. Patients diagnosed with sIVH had significantly more often lower gestational age (p = 0.0005), lower birth weight (p = 0.01), lack of antenatal steroid therapy (p = 0.0004), a partial course of antenatal steroid therapy (p = 0.0009), a lower Apgar score at 1 minute (p < 0.0001), invasive mechanical ventilation use (p < 0.0001) and a lower hematocrit (Hct) level at the first measurement after birth (p = 0.002). After multivariate analysis, the significant risk factors that increased risk of sIVH were: lack of or only a partial course of antenatal steroid therapy (OR: 2.85; 95% CI: 1.18–6.83, p = 0.02, OR: 3.16; 95% CI: 1.49–6.67, p = 0.003 respectively), invasive mechanical ventilation use (OR: 4.75; 95% CI: 2.18–10.34, p < 0.001) and the Hct level < 45% at first measurement (OR: 2.62; 95% CI: 1.28–5.37, p = 0.008). Conclusions The occurrence of any grade, but especially a severe grade of IVH is still a very common problem in premature infants. Administering a full course of antenatal steroid therapy, applying interventions to prevent anemia in preterm infants and optimizing ventilation methods may help reduce the incidence of IVH, which will improve patient outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".