Neonatal past catches up when COVID-19 comes to town
Bibliographic record
Abstract
Congenital anomalies are structural or functional changes that are present at birth. Whilst each is rare, together they are common, occurring in one in every 33 babies born in the United States. 1 Importantly, congenital anomalies remain a leading cause of severe childhood mortality, morbidity, and disability, especially in infancy. In adults, the presence of underlying medical conditions is known to increase the risk of severe illness from COVID-19, 2 but less is known about the risk factors in children. This is unfortunate as the impact of severe illness in children can be long-lasting. Congenital heart disease has been shown to lead to more severe COVID-19 outcomes. 3 Whether other congenital anomalies predispose children to severe COVID-19 remains less well-understood, but the higher rates of severe illness from other common childhood infections, such as respiratory syncytial virus, 4 prompt further reflection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".