1113 3D Digital Capture of Head Circumference and Volume in Neonates - A Method Evaluation
Bibliographic record
Abstract
Background Manual measurement of head circumference (HC) is used to quantify head growth in preterm infants. Laser shape digitizers offer semi-automatic HC measuring and additional information on head volume (HV). Reliability and accuracy in obtaining HC and HV in neonates has not been investigated yet. Aims To determine intraobserver and interobserver variability of HC and HV measurements in neonates with a 3D digital capture system. To compare the method with manual HC measurements. Methods Standard weekly HC measurements on a neonatal unit were conducted manually and digitally with STARScanner laser shape digitizer (Vorum Research Corp., Vancouver, BC) over 12 months. Method comparison was performed using Passing-Bablok-Regression (PBR), Cusum test and Bland-Altman (BA) analyses. Multiple scan examinations by different trained observers were performed to obtain intraobserver/interobserver data. Results Intraobserver coefficient of variation was low for HC (0.1–0.9%) and HV (0.54–1.1%). BA (mean percentage of difference Md ; 95% CI) of interobserver data showed interchangeability for HC (Md –0.005; CI-0.39–0.39) and HV (Md 1.51; CI –1.17–4.1). 2. Method comparison data was acquired from 446 measurements in 258 infants (HC 318±19.5mm). Overall agreement was good (Md –0.82; CI –4.89–3.24), PBR showed no significant systematic or proportional differences (a=1.03, CI 0.99–1.06; b= –7.06 CI –17.7–3.01). There was no significant deviation from linearity (p=0.62). Conclusions Infant head shape capturing with the examined device is reliable, accurate and save. It offers additional information on HV. Possible benefits of HV in quantifying head growth in preterm infants need to be further investigated.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".