Hutcheon and Platt Respond to "The Hidden Population in Perinatal Epidemiology"
Bibliographic record
Abstract
We thank Dr. Paneth for his insightful comments (1) and, in particular, the way in which he has located our work on fetal growth (2) in the context of the larger body of work on fetuses at risk. We would like to respond to some of the concerns raised regarding the measurement of intrauterine growth, as well as touch briefly on the larger issue of selecting denominators in perinatal epidemiology. We agree entirely with Dr. Paneth's criticism (1) of the focus on dichotomous measures of fetal growth (“small-for-gestational-age” vs. “appropriate-for-gestational-age”) instead of continuous ones such as birth weight z scores. However, as with percentiles, we argue that it is important to ensure that the mean weights (and standard deviations) used to calculate z scores are based on the average weights of all fetuses that progressed to a given gestational age, not the average weights of only fetuses subsequently born that week. We agree that epidemiologists would be well served to respect the natural continuum of fetal growth in their attempts to better understand the etiology of growth restriction, but they should do so with a measure that is not associated with gestational age at birth.
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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 | 0.051 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.068 | 0.081 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".