The predictive ability of conditional fetal growth percentiles
Bibliographic record
Abstract
Conditional fetal growth percentiles are percentiles that are calculated taking into account (conditional on) an infant's weight earlier in pregnancy. Although they have been proposed in the statistical literature as a more methodologically appropriate method of measuring fetal growth, their ability to predict adverse perinatal outcomes due to fetal growth restriction is unknown. Using a large, unselected clinical ultrasound database at the Royal Victoria Hospital in Montreal, Canada, we calculated conditional growth percentiles for infants' weight at birth, given their weight at the time of a routine 32- or 33-week ultrasound. The risk of adverse perinatal outcome (perinatal mortality, low Apgar, acidaemia, or seizures/organ failure due to asphyxia) among small-for-gestational-age infants (SGA) as established by conditional growth percentiles was calculated as well as the risk among infants classified as SGA by conventional weight-for-gestational-age percentiles. Regardless of the threshold used to define SGA (fifth, 10th, 15th, 20th), conditional percentiles did not appear to improve the identification of adverse perinatal outcomes compared with conventional weight-for-gestational-age charts. Further work is needed to confirm our results as well as to explore potential reasons for the lack of benefits from using a measure of growth instead of size to identify fetal growth restriction.
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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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".