Misclassification due to age grouping in measures of child development
Bibliographic record
Abstract
PURPOSE: Screens for developmental delay generally provide a set of norms for different age groups. Development varies continuously with age, however, and applying a single criterion for an age range will inevitably produce misclassifications. In this report, we estimate the resulting error rate for one example: the cognitive subscale of the Bayley Scales of Infant and Toddler Development (BSID-III). DESIGN: Data come from a general population sample of 594 children (305 male) aged 1 month to 42.5 months who received the BSID-III as part of a validation study. We used regression models to estimate the mean and variance of the cognitive subscale as a function of age. We then used these results to generate a dataset of one million simulated participants and compared their status before and after division into age groups. Finally, we applied broader age bands used in two other instruments and explored likely validity limitations when different instruments are compared. RESULTS: When BSID-III age groups are used, 15% of cases are missed and 15% of apparent cases are false positives. Wider age groups produced error rates from 27% to 46%. Comparison of different age groups suggests that sensitivity in validation studies would be limited, under certain assumptions, to 70% or less. IMPLICATIONS: The use of age groups produces a large number of misclassifications. Although affected children will usually be close to the threshold, this may lead to misreferrals. Results may help to explain the poor measured agreement of development screens. Scoring methods that treat child age as continuous would improve instrument accuracy.
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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 | 0.072 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".