Validation of Parent-reported Gestational Age Categories for Children Less Than 6 Years of Age
Bibliographic record
Abstract
BACKGROUND: Preterm birth is an important outcome or exposure in epidemiologic research. When administrative data on measured gestational age is not available, parent-reported gestational age can be obtained from questionnaires, which is subject to potential bias. To our knowledge, few studies have assessed the validity of parent-reported gestational age categories, including commonly defined categories of preterm birth. METHODS: We used linked data from primarily healthy children <6 years of age in TARGet Kids! in Toronto, Canada, and ICES administrative healthcare data from April 2011 to March 2020. We assessed the criterion validity of questionnaire-based parent-reported gestational age by calculating sensitivity and specificity for term (≥37 weeks), late preterm (34-36 weeks), and moderately preterm (32-33 weeks) gestational age categories, using administrative healthcare records of gestational age as the criterion standard. We conducted subgroup analyses for various parent and socioeconomic factors that may influence recall. RESULTS: Of the 4684 participants, 97.3% correctly classified the gestational age category according to administrative healthcare data. Parent-reported gestational age sensitivity ranged from 83.7% to 98.5% and specificity ranged from 88.3% to 99.8%, depending on category. For each subgroup characteristic, sensitivity and specificity were all ≥70%. Lower educational attainment, lower family income, father reporting, ≥1 year since birth, ≥2 children, lower parent age, and reported gestational diabetes and/or hypertension were associated with slightly lower sensitivity and/or specificity. CONCLUSIONS: In this linked cohort, parent-reported gestational age categories had high accuracy. Criterion validity varied minimally among some parent and socioeconomic factors. Our findings can inform future quantitative bias analyses.
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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 | 0.002 | 0.002 |
| 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".