Classification Performance of the Ages and Stages Questionnaire: Influence of Maternal Education Level
Bibliographic record
Abstract
(1) Background: The Ages and Stages Questionnaire—Third Edition (ASQ-3) is a parental screening questionnaire increasingly being used to evaluate the development of preterm children. We aimed to assess the classification performance of the ASQ-3 in preterm infant follow-up. (2) Methods: In this cross-sectional study, we included 185 children from the SEVE longitudinal cohort born <33 weeks of gestational age between November 2011 and January 2018, who had both an ASQ-3 score at 24 months of corrected age (CA) and a revised Brunet–Lézine (RBL) scale score at 30 months of CA. The ASQ-3 overall score and sub-scores were compared to the RBL developmental quotient (DQ) scores domain by domain. The diagnostic performance of the ASQ-3 was evaluated with the RBL as the reference method by calculating sensitivity, specificity, and positive and negative likelihood ratios. A multivariate analysis assessed the association between low maternal education level and incorrect evaluation with the ASQ-3. (3) Results: The ASQ-3 overall score had a specificity of 91%, a sensitivity of 34%, a positive likelihood ratio of 3.82, and a negative likelihood ratio of 0.72. Low maternal education level was a major risk factor for incorrectly evaluating children with the ASQ-3 (odds ratio 4.16, 95% confidence interval 1.47–12.03; p < 0.01). (4) Conclusions: Regarding the low sensitivity and the impact of a low maternal education level on the classification performance of the ASQ-3, this parental questionnaire should not be used alone to follow the development of preterm children
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".