New Reference Curves for Head Circumference at Birth, by Gestational Age
Bibliographic record
Abstract
BACKGROUND: The measurement of head circumference (HC) at birth reflects intrauterine brain development. HC charts currently used in Canada are either dated, mixed-gender, nonrepresentative of lower gestational ages (GAs), or reflective of other populations. METHODS: To create both birth weight and HC curves, we combined weight and HC data from the Canadian Neonatal Network (CNN) database (admissions in NICUs across Canada) with McGill's Obstetrical Neonatal Database (MOND; all births at a tertiary hospital in Montreal, Canada). We included CNN data for GAs of 23 to 34 weeks (2003-2007) and MOND data for GAs of 35 to 41 weeks (1995-2006). Nonsingletons, congenital anomalies, and measurements greater than ±4 SD from the mean were excluded. Distributions of birth weight and HC at each GA were statistically (penalized spline regression) smoothed. Birth weight curves were compared with recent Canadian reference curves and HC curves with historical and/or frequently used curves. RESULTS: We included 39,896 births (3121 births at <30 weeks' GA) to generate the curves. Current weight curves were similar to Canadian reference charts for both genders. Weight and HC measurements in boys were higher than in girls. When classified according to recent international references, the proportion of CNN-MOND infants at ≥32 weeks' GA with HCs <10th percentile was significantly underestimated. When classified according to historical reference curves, a significant number of CNN-MOND infants of all GAs with HCs <10th and >90th percentiles were misclassified. CONCLUSIONS: We developed recent gender-specific reference curves for HC at birth for singletons at 23 to 41 completed weeks' GA, which included a large number of very premature infants, reflecting the current geotemporal Canadian population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".