Rates of detection of developmental problems at the 18‐month well‐baby visit by family physicians' using four evidence‐based screening tools compared to usual care: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Early and regular developmental screening can improve children's development through early intervention but is insufficiently used. Most developmental problems are readily evident at the 18-month well-baby visit. This trial's purpose is to: (1) compare identification rates of developmental problems by GPs/family physicians using four evidence-based tools with non-evidence based screening, and (2) ascertain whether the four tools can be completed in 10-min pre-visit on a computer. METHODS: We compared two approaches to early identification via random assignment of 54 families to either: 'usual care' (informal judgment including ad-hoc milestones, n = 25); or (2) 'Evidence-based' care (use of four validated, accurate screening tools, n = 29), including: the Parents' Evaluation of Developmental Status (PEDS), the PEDS-Developmental Milestones (PEDS-DM), the Modified Checklist for Autism in Toddlers (M-CHAT) and PHQ9 (maternal depression). RESULTS: In the 'usual care' group four (16%) and in the evidence-based tools group 18 (62%) were identified as having a possible developmental problem. In the evidence-based tools group three infants were to be recalled at 24 months for language checks (no specialist referrals made). In the 'usual care' group four problems were identified: one child was referred for speech therapy, two to return to check language at 24 months and a mother to discuss depression. All forms were completed on-line within 10 min. CONCLUSIONS: Despite higher early detection rates in the evidence-based care group, there were no differences in referral rates between evidence-based and usual-care groups. This suggests that clinicians: (1) override evidence-based screening results with informal judgment; and/or (2) need assistance understanding test results and making referrals. Possible solutions are improve the quality of information obtained from the screening process, improved training of physicians, improved support for individual practices and acceptance by the regional health authority for overall responsibility for screening and creation of a comprehensive network.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".