Can We Predict Post-Hemorrhagic Ventricular Dilatation in Preterm Infants with Severe Intraventricular Hemorrhage?
Bibliographic record
Abstract
Abstract BACKGROUND: The incidence of post-hemorrhagic ventricular dilatation (PHVD) remains high in preterm infants. Little is known about the risk factors for PHVD in infants with severe intraventricular hemorrhage (IVH). OBJECTIVES: To determine the predictors of PHVD among preterm infants with severe IVH. DESIGN/METHODS: We conducted a retrospective review of all pre-term infants (22+0 - 32+6 weeks) who were admitted to NICUs participating in the Canadian Neonatal Network between 2010 and 2014. Infants with severe IVH (IVH with ventricular dilatation or parenchymal bleeding) who survived ≥ 72 hours were included. Perinatal and neonatal risk factors were compared between infants with and without PHVD (lateral ventricles >10 mm). RESULTS: Of 16600 eligible infants, 1964 (11.8%) developed severe IVH. Of 1815 infants with severe IVH who survived ≥72 hours, 616 (34%) developed PHVD. Factors associated with occurrence of PHVD include: lower gestational age, small for gestational age, low 5 minute Apgar score, SNAPII score>20, surfactant therapy, high frequency oscillatory ventilation (HFOV), inotropes and occurrence of pneumothorax. [table 1]. There were no differences between both groups in relation to antenatal steroids, multiple pregnancy, mode of delivery, birth weight, gender or the proportion received prophylactic indomethacin. Multivariate analysis showed low five-minute Apgar score and HFOV to be independent predictors of PHVD while maternal magnesium sulfate and small for gestation (SGA) to be protective against PHVD.[table 2]. CONCLUSION: Our study identified factors involved in the prediction of PHVD in a national cohort of preterm infants. The mechanisms by which these factors may impact PHVD need further investigation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".