Lowering Developmental Screening Thresholds and Raising Quality Improvement for Preterm Children
Bibliographic record
Abstract
OBJECTIVES: To determine: 1) if preterm children were referred, identified and received early intervention (EI)/ early childhood special education (ECSE) services at rates equivalent to term children after implementation of a universal, periodic Ages and Stages Questionnaire (ASQ) surveillance and screening system; 2) if pediatricians sufficiently lowered their screening thresholds with preterm children;and 3) if quality improvement opportunities exist. PATIENT AND METHODS: Secondary analysis was performed on 64 lower-risk, mostly late-preterm and 1363 term children who originally presented to their 12- or 24-month well- visits. Higher-risk preemies already involved with an EI agency/ identified with a delay were excluded. Board-certified pediatricians (N=18), and nurse practitioners (N = 2), blind to the ASQ results, were secondary participants. Differences between preterm and term developmental agency referrals were examined comparing Pediatric Developmental Impression to the ASQ under natural clinic conditions using a combined in-office or mail-back data collection protocol. Medical record and county EI/ECSE follow-up outcomes were conducted at 36 to 60 months. RESULTS: At 12 and 24 months, preterm (versus term) referral rates were 9.5%(versus 5.6%) with Pediatric Developmental Impression and 26.2% (versus 8.1%) with the ASQ. By 36 to 60 months, 37.5% of preterm (20.8% term) children were referred to EI/ECSE; of which, 50.0% of preterm (42.4% term) children were eligible for services, 54.2%of preterm children were identified with a developmental-behavioral disorder and 29.2% of preterm (20.8% term) children did not follow-up. For ASQ-only preterm referrals,55.6% were subsequently diagnosed with a developmental delay and/or disorder.Preterm children were 2 times more likely to be eligible than term children [corrected]. CONCLUSIONS: Combined referral, quality improvement and outcome data suggests that clinicians should lower their threshold for administering a psychometrically sound developmental screen when providing surveillance for ex-preemies. Quality improvement opportunities exist with diligent developmental surveillance and a more collaborative, standardized, reliable and interpersonal referral process.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".