Detecting and eliminating erroneous gestational ages: a normal mixture model
Bibliographic record
Abstract
In perinatal research and clinical practice, gestational age is a crucial variable for measuring foetal 'growth' (birth weight for gestational age) and for estimating the risk of mortality and morbidity, yet reported gestational age values are affected by random and systematic errors due to the absence of a gold standard measure. Previous investigators have used birth weight (which is measured with greater validity and precision than is gestational age) to correct such errors, but existing methods are inadequate due to unreasonable assumptions about the distributions of birth weight and gestational age. We propose a new method for identifying and correcting implausible observations using the expectation-maximization (EM) algorithm. Using population-based data from U.S. birth certificates, we compare the resulting gestational ages, birth weight distributions at each gestational age, and gestational age-specific infant mortality based on the new method with those on the same population produced by previous published correction methods. The new method gives the best birth weight distributions for gestational age and the most realistic gestational-age-specific mortality rates, while each of the other methods has at least one significant flaw.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".