93 Timing and frequency of head ultrasound screening to identify severe neurological injury among very preterm infants
Bibliographic record
Abstract
Abstract Primary Subject area Neonatal-Perinatal Medicine Background Universal head ultrasound (HUS) screening for severe neurological injury (SNI) injury is recommended for infants born < 32 weeks’ gestational age (GA). However, the risk of SNI varies inversely with GA at birth and other known risk factors; therefore targeted screening may be more appropriate. Objectives The objective of the study is to develop a risk-stratified HUS screening protocol for infants born < 32 weeks’ to identify SNI accurately while minimizing resource use. Design/Methods Retrospective cohort study of infants born 23-31 weeks’ admitted to a tertiary NICU between 2011-2017. Patient characteristics were extracted from the Canadian Neonatal Network database. All HUS were individually reviewed by a trained abstractor and grouped based on date of exam relative to birth: ≤ 3 days, 4-7 days, 8-14 days, 28-42 days and 35-42 weeks’ corrected GA. Severe neurological injury was defined as intraventricular hemorrhage grade ≥ 3 and/or periventricular leukomalacia on HUS. Logistic regression models were used to identify perinatal risk factors for SNI and determine the number and timing of HUS with highest diagnostic accuracy. Results Of 651 infants included, 72 (11%) developed SNI. Independent risk factors for SNI were GA <29 weeks (AOR 3.09, 95% CI 1.65-6.08), vasopressors (AOR 2.95, 95% CI 1.24-6.80) and mechanical ventilation on day of admission (AOR 2.22, 95% CI 1.23-4.11). Infants were grouped into three screening groups based on their exposure to these risk factors (Table 1). Diagnostic accuracy of 63 models of combinations of HUS time points were assessed, and a screening protocol was developed based on the specific time points of HUS that maximized diagnostic accuracy (area under the ROC curve >0.9) while minimizing number of HUS for each screening group (Table 2). Using this protocol could reduce the total number of HUS performed by 920 (40%) and median number of HUS per infant from three (IQR 2-4) to 2 (IQR 1-2; p < 0 .001). Conclusion Implementation of a risk factor-based HUS screening protocol may reduce resource use while maintaining high diagnostic accuracy for SNI, and reflects choosing wisely in the NICU.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".