Influence of different methods for calculating gestational age at birth on prematurity and small for gestational age proportions: a systematic review with meta-analysis
Bibliographic record
Abstract
Abstract Background Recognizing premature newborns and small-for-gestational-age (SGA) is essential for providing care and supporting public policies. This systematic review aims to identify the influence of the last menstrual period (LMP) compared to ultrasonography (USG) before 24 weeks of gestation references on prematurity and SGA proportions at birth. Methods Systematic review with meta-analysis followed the recommendations of the PRISMA Statement. PubMed, BVS, LILACS, Scopus-Elsevier, Embase-Elsevier, and Web-of-Science were searched (10–30-2022). The research question was: (P) newborns, (E) USG for estimating GA, (C) LMP for estimating GA, and (O) prematurity and SGA rates for both methods. Independent reviewers screened the articles and extracted the absolute number of preterm and SGA infants, reference standards, design, countries, and bias. Prematurity was birth before 37 weeks of gestation, and SGA was the birth weight below the p10 on the growth curve. The quality of the studies was assessed using the New-Castle-Ottawa Scale. The difference between proportions estimated the size effect in a meta-analysis of prevalence. Results Among the 642 articles, 20 were included for data extraction and synthesis. The prematurity proportions ranged from 1.8 to 33.6% by USG and varied from 3.4 to 16.5% by the LMP. The pooled risk difference of prematurity proportions revealed an overestimation of the preterm birth of 2% in favor of LMP, with low certainty: 0.02 (95%CI: 0.01 to 0.03); I2 97%). Subgroup analysis of USG biometry (eight articles) showed homogeneity for a null risk difference between prematurity proportions when crown-rump length was the reference: 0.00 (95%CI: -0.001 to 0.000; I2: 0%); for biparietal diameter, risk difference was 0.00 (95%CI: -0.001 to 0.000; I2: 41%). Only one report showed the SGA proportions of 32% by the USG and 38% by the LMP. Conclusions LMP-based GA, compared to a USG reference, has little or no effect on prematurity proportions considering the high heterogeneity among studies. Few data (one study) remained unclear the influence of such references on SGA proportions. Results reinforced the importance of qualified GA to mitigate the impact on perinatal statistics. Trial registration Registration number PROSPERO: CRD42020184646.
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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 | 0.051 | 0.137 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.066 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".