Development of a Neuropsychological Service Pathway for School-Age Neonatal Follow Up: a Feasibility Pilot
Bibliographic record
Abstract
OBJECTIVE: Neuropsychological care benefits children with neonatal brain conditions by monitoring brain health and directing supports to optimize learning and psychosocial growth. Conventional assessments often are time and resource intensive; innovative, precise, and efficient assessment models are needed. This study examined 1) feasibility and acceptability of a tiered assessment protocol and 2) preliminary predictive validity of screening measures. METHOD: Children aged 4-18 years and their caregivers were referred for neuropsychological evaluation. A tiered protocol was co-designed with family advisors and included 3 steps: 1) invitation to consultation, including psychoeducation; 2) screening measures of cognition, learning, and well-being; and 3) virtual 1:1 consultation with a neuropsychologist/fellow. Collaborative caregiver-clinician discussion led to a) in-person testing, b) deferred assessment, or c) discharge. A portion (22%) of cases were reviewed independently to assess inter-clinician reliability. Caregiver feedback was gathered via survey. Multiple linear regression models assessed relationships between screening measures and 1) consultation outcome, and 2) child full scale intellectual IQ scores (FSIQ). RESULTS: Of 104 families invited to participate, 98 (94%) responded: 9 (9.2%) declined services and 85 (86.7%) completed screening measures and consultation. After consultation, 50 (59%) proceeded to in-person testing, 17 (20%) deferred assessment, and 18 (21%) were discharged. Inter-clinician agreement on consultation outcome was strong (90%). Caregiver feedback reflected protocol feasibility and acceptability; comments described helpfulness of monitoring, and timeliness of process. Screening measures predicted outcomes of consultation and child FSIQ. CONCLUSIONS: Results illustrate importance of evaluating neuropsychological pathways with precision, efficiency and caregiver-partners in mind.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".