Comparison of parent or caregiver-completed development screening tools with Bayley Scales of Infant Development: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Parent/caregiver-completed developmental testing (PCDT) is integral to developmental care in children; however, there is limited information on its accuracy. In this systematic review, we compared the diagnostic accuracy of PCDT with concurrently administered Bayley Scales of Infant Development for detection of developmental delay (DD) in children below 4 years of age. METHODS: We searched databases PubMed, Embase, CINAHL, PsycINFO and Google Scholar until November 2023. Bivariate and multiple thresholds summary receiver operating characteristics were used to obtain the summary sensitivity and specificity with 95% CIs. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used for risk of bias assessment. RESULTS: A total of 38 studies (31 in the meta-analysis) were included. Ages and Stages Questionnaire (ASQ) and Parent Report of Children's Abilities-Revised (PARCA-R) were the most commonly evaluated PCDTs. ASQ score >2 SD below the mean had an overall sensitivity of 0.72 (0.6, 0.82) and 0.63 (0.50, 0.75) at a median specificity of 0.89 (0.82, 0.94) and 0.81 (0.76, 0.86) for diagnosing moderate to severe DD and severe DD, respectively. PARCA- R had an overall sensitivity of 0.69 (0.51, 0.83) at median specificity of 0.75 (0.64, 0.83) for predicting severe DD. Participant selection bias and partial verification bias were found in over 50% of the studies. The certainty of evidence was low for the studied outcomes. CONCLUSIONS: The most commonly studied parental tools, ASQ and PARCA-R, have moderate to low sensitivity and moderate specificity for detecting DD in young children. High risk of bias and heterogeneity in the available data can potentially impact the interpretation of our results. PROSPERO REGISTRATION NUMBER: CRD42021268629.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.039 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".