Should Fetal Growth Charts Be References or Standards?
Bibliographic record
Abstract
BACKGROUND: Fetal growth standards (prescriptive charts derived from low-risk pregnancies) are theoretically better tools to monitor fetal growth than conventional references. We examined how modifying chart inclusion criteria influenced the resulting curves. METHODS: We summarized estimated fetal weight (EFW) distributions from a hospital's routine 32-week ultrasound in all nonanomalous singleton fetuses (reference) and in those without maternal-fetal conditions affecting fetal growth (standard). We calculated EFWs for the 3rd, 5th, 10th, and 50th percentiles, and the proportion of fetuses each chart classified as small for gestational age. RESULTS: Of 2309 fetuses in our reference, 690 (30%) met the standard's inclusion criteria. There were no meaningful differences between the EFW distributions of the reference and standard curves (50th percentile: 1989 g reference vs. 1968 g standard; 10th percentile: 1711 g reference vs. 1710 g standard), or the proportion of small for gestational age fetuses (both 9.9%). CONCLUSIONS: In our study, there was little practical difference between a fetal growth reference and standard for detecting small infants.
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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 | 0.160 | 0.588 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".