Third trimester growth restriction patterns: individualized assessment using a fetal growth pathology score
Bibliographic record
Abstract
OBJECTIVE: To qualitatively and quantitatively characterize third trimester growth patterns in fetuses/neonates with growth restriction using Individualized Growth Assessment. METHODS: Serial fetal size measurements from 73 fetuses with proven growth restriction were evaluated using a novel composite parameter, the Fetal Growth Pathology Score (FGPS1). Third trimester FGPS1 measurements plotted against fetal age were examined for patterns. Identified patterns were characterized using the four components of the FGP1 [head circumference (HC), abdominal circumference (AC), femur diaphysis length (FDL), estimated weight (EWT)]. A secondary characterization using age of onset, duration and magnitude of the growth abnormality process was also performed. Frequencies and magnitudes of abnormal values in different FGPS1 patterns were compared. RESULTS: Five growth restriction patterns were found in 70/73 (95.9%) of the cases, with progressive worsening [Pattern 1 (37.0%)] and abnormal growth identified only at last scan [Pattern 2 (27.4%)] being the most common. These two patterns were usually statistically different from each other and the other three with respect to size parameter abnormalities and abnormal growth process characteristics (MANOVA). Growth abnormalities in all parameters of the FGPS1 contributed to the five abnormality patterns although AC and EWT were most important. The age of onset, duration and magnitude were similar between patterns except for Pattern 2, which had a late onset and a short duration (GLM + contrasts). CONCLUSIONS: Our study represents the first detailed evaluation of third trimester growth restriction using methods that consider the growth potential of each fetus. Five distinctive and repetitive patterns were found, suggesting that fetal growth restriction evolves in different ways. Further research is needed to determine the relationships of these patterns to physiological/biochemical changes and adverse outcomes associated with 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".