1080 3D Digital Capture of Head Growth in Neonates - Correlation of Head Circumference and Head Volume
Bibliographic record
Abstract
Background Head circumference (HC) is measured in newborns to evaluate head growth. It is not known, whether HC is always an appropriate measure of head volume (HV). Digital capture of the neonatal head offers information on HC and HV. Aims To determine overall correlation of HC and HV and with regard to postmenstrual age (PMA) and with regard to the actual body weight (BW). Methods Head measurements with STARscanner laser shape digitizer (Vorum research Corp., Vancouver, BC) were performed in preterm infants prior to discharge over a 12 month period. Data on HC and HV were calculated with STARscanner Laser Data Acquisiton System (Orthomerica, Orlando, FL) and analyzed in different subgroups. Results Included were 243 neonates at time of discharge (mean HC 32.8±1.9 cm, mean HV 356.7±64.3 ml). a) There was an overall correlation between HC and HV (r=0.90, R²=0.81, p<0.001). Correlation between HC and HV was: b) in infants with a PMA < 37 (r= 0.71, R²=0.52, p=0.001) vs. PMA > 37 weeks (r=0.92, R²=0.85, p<0.001) and c) in BW < 2500g (r=0.69, R²= 0.49, p=0.04) vs. BW >2500g (r=0.88, R²=0.77, p<0.001). Conclusions Neonates with comparable HC can show very different HV, especially in infants with low PMA or BW. Thus additional measurement of HV enables to detect variable patterns of head growth and shape. Underlying causes and the meaning for neurological outcome need to be determined.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".