Customized birth‐weight centiles and placenta‐related fetal growth restriction
Bibliographic record
Abstract
ABSTRACT Objective The value of using customized birth‐weight centiles to improve the diagnostic accuracy for fetal growth restriction (FGR), in comparison with using population‐based charts, remains a matter of debate. One potential explanation for the conflicting data is that most studies used measures of perinatal mortality and morbidity as proxies for placenta‐mediated FGR, many of which are not specific and may be confounded by other factors such as prematurity. The aim of this study was to compare the diagnostic accuracy of small‐for‐gestational age (SGA) at birth, defined according to customized vs population‐based charts, for associated abnormal placental pathology. Methods This was a secondary analysis of data from a prospective cohort study on risk factors for placenta‐mediated complications and abnormal placental pathology in low‐risk nulliparous women. All placentae were sent for detailed histopathological examination by two perinatal pathologists. The primary exposure was SGA, defined as birth weight < 10th centile for gestational age using either a customized (SGAcust) or a population‐based (SGApop) birth‐weight reference. The outcomes of interest were one of three types of abnormal placental pathology associated with FGR: maternal vascular malperfusion (MVM), chronic villitis and fetal vascular malperfusion (FVM). Adjusted relative risks (aRR) with 95% CIs were estimated using modified Poisson regression analysis, with adjustment for smoking, body mass index and aspirin treatment. Results A total of 857 nulliparous women met the study criteria. The proportions of infants identified as SGA based on the customized and population‐based charts were 12.6% (108/857) and 11.4% (98/857), respectively. A diagnosis of SGA using either customized or population‐based charts was associated with an increased risk of any placental pathology (aRR, 3.04 (95% CI, 2.29–4.04) and 1.60 (95% CI, 1.10–2.31), respectively) and MVM pathology (aRR, 12.33 (95% CI, 6.60–23.03) and 5.29 (95% CI, 2.87–9.76), respectively). SGAcust, but not SGApop, was also associated with an increased risk for chronic villitis (aRR, 1.85 (95% CI, 1.07–3.18)) and FVM pathology (aRR, 2.48 (95% CI, 1.25–4.93)). SGAcust had a higher detection rate for any placental pathology (30.3% vs 17.1%; P < 0.001), MVM pathology (63.2% vs 39.5%; P = 0.003) and chronic villitis (20.8% vs 8.3%; P = 0.007) than did SGApop, for a similar false‐positive rate. This was mainly the result of a higher detection rate for abnormal pathology in the white and East‐Asian subgroups and a lower false‐positive rate for abnormal pathology in the South‐Asian subgroup by SGAcust than by SGApop. In addition, pregnancies in the SGAcust group, but not those in the SGApop group, were more likely to be complicated by preterm birth and a low 5‐min Apgar score than were the corresponding non‐SGA group. Conclusion These findings suggest that customized birth‐weight centiles may be superior to population‐based birth‐weight centiles in detecting FGR that is due to underlying placental disease. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".