Author response: External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh
Bibliographic record
Abstract
Complications from preterm birth are the leading cause of death among children under five. Ultrasounds are routinely used in wealthy countries to track babies' development. In countries with limited resources, however, ultrasounds are rare, making it harder to estimate how many children are born prematurely. Blood tests may offer a way to determine whether a newborn was born too early when ultrasounds are not available. Many countries already require clinicians to collect a drop of blood from newborns via a heel-prick or from their umbilical cord. Testing these blood spots identifies babies at risk of rare conditions so they can receive prompt treatment. Chemicals in the blood vary depending on how long the newborn spent growing in its mother’s womb. Scientists have developed a mathematical formula that can estimate a baby’s gestational age based on these chemicals. Using blood spots to estimate gestational age worked well when this strategy was tested in Canada, a high-income country. More tests are needed to determine if it works in low-income countries. Now, Murphy et al. show their blood spot-testing strategy also reliably predicts the gestational age of babies in Matlab, Bangladesh. In the experiments, blood spots were collected from 1,069 newborns. This included 1,036 cord blood samples and 487 heel prick samples. Nearly all the samples came from full-term infants. A mathematical model estimated the infants' gestational age to within an average of one week of their true age when applied to heel-prick blood samples and to within two weeks of the baby’s true gestational age 94% of the time. The model also provided reliable estimates of babies’ gestational ages when cord blood samples were tested, which is useful as the Bangladeshi parents were more comfortable with this method of blood collection. Using this strategy to estimate how many babies are born too early in low-income countries may help the countries develop strategies to reduce preterm births. The estimates might also help identify preterm babies who need special care.
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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 | 0.017 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".