Misclassification of Newborns Due to Systematic Error in Plotting Birth Weight Percentile Values
Bibliographic record
Abstract
OBJECTIVES: Higher than expected small for gestational age (SGA) rates and lower than expected large for gestational age (LGA) rates have been observed. A possible explanation is a leftward shift of percentile curves for birth weight due to a systematic error in plotting birth weight values in charts (ie, plotting weekly mean birth weight data at the beginning of the weeks). Our objectives were to assess how common this plotting error is and to analyze the effect of this error on SGA and LGA classification based on data from the German perinatal survey. METHODS: First, a systematic literature search for birth weight charts was performed, and the charts were analyzed for the plotting error. Second, percentile values (10th, 50th, and 90th) for 25 to 42 completed weeks of gestation were calculated from the data of 1181200 male singleton newborns (German perinatal survey, 1995-2000). Birth weight percentile curves were calculated with and without the plotting error, and the resulting SGA and LGA rates were analyzed. RESULTS: Fourteen of the 16 identified publications contained the systematic error in plotting. Using our calculated percentile curves, a leftward shift caused by the plotting error led to an SGA rate of 12.5% and an LGA rate of 7.7%; ∼5% of newborns were misclassified. CONCLUSIONS: Percentile charts should be examined for the described systematic error and, if necessary, corrected.
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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 | 0.038 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".