Validity of Newborn Clinical Assessment to Determine Gestational Age in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Gestational age (GA) is frequently unknown or inaccurate in pregnancies in low-income countries. Early identification of preterm infants may help link them to potentially life-saving interventions. METHODS: We conducted a validation study in a community-based birth cohort in rural Bangladesh. GA was determined by pregnancy ultrasound (<20 weeks). Community health workers conducted home visits (<72 hours) to assess physical/neuromuscular signs and measure anthropometrics. The distribution, agreement, and diagnostic accuracy of different clinical methods of GA assessment were determined compared with early ultrasound dating. RESULTS: In the live-born cohort (n = 1066), the mean ultrasound GA was 39.1 weeks (SD 2.0) and prevalence of preterm birth (<37 weeks) was 11.4%. Among assessed newborns (n = 710), the mean ultrasound GA was 39.3 weeks (SD 1.6) (8.3% preterm) and by Ballard scoring the mean GA was 38.9 weeks (SD 1.7) (12.9% preterm). The average bias of the Ballard was -0.4 weeks; however, 95% limits of agreement were wide (-4.7 to 4.0 weeks) and the accuracy for identifying preterm infants was low (sensitivity 16%, specificity 87%). Simplified methods for GA assessment had poor diagnostic accuracy for identifying preterm births (community health worker prematurity scorecard [sensitivity/specificity: 70%/27%]; Capurro [5%/96%]; Eregie [75%/58%]; Bhagwat [18%/87%], foot length <75 mm [64%/35%]; birth weight <2500 g [54%/82%]). Neonatal anthropometrics had poor to fair performance for classifying preterm infants (areas under the receiver operating curve 0.52-0.80). CONCLUSIONS: Newborn clinical assessment of GA is challenging at the community level in low-resource settings. Anthropometrics are also inaccurate surrogate markers for GA in settings with high rates of 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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".