Predictors of cognitive, behavioural and academic difficulties in NF1
Bibliographic record
Abstract
Aims The aim of this study is to systematically investigate the demographic and disease predictors of cognitive and behavioural phenotype in the largest cohort of children with NF1 published to date. Based on previously published research, we examine the potential role of demographic predictors such as age, sex, SES, parental NF1 status as well as the neurological complications such as epilepsy and brain tumours in NF1 associated cognitive/ behavioural impairments. Method In this cross-sectional study design, participant data were drawn from two large databases which included (i) A clinical database of all patients with NF1 seen in a clinical psychological service from 2010 to 2019 and (ii) A research dataset from two previously published studies (2,8). The complex National NF1 service based within Manchester regional genetic services is set up for individuals with complex NF1 (https://www.mangen.co.uk/healthcare-professionals/clinical-genomic-services/nf1/) in the North of the UK. Children were referred to the psychological services by NF1 clinicians if psychological assessment was warranted based on parental reports. In order to reduce clinic referral bias, the clinical sample was supplemented by including participants that were seen solely for the purposes of research studies within our centre. Result Relative to population norms, 90% of the NF1 sample demonstrated significantly lower scores in at least one cognitive or behavioral domain. Family history of NF1 and lower SES were independently associated with poorer cognitive, behavioral and academic outcomes. Neurological problems such as epilepsy and hydrocephalus were associated with lower IQ and academic skills. Conclusion Cognitive and behavioural phenotypes commonly emerge via a complex interplay between genes and environmental factors, and this is true also of a monogenic condition such as NF1. Early interventions and remedial education may be targeted to risk groups such those with familial NF1, families with lower SES and those with associated neurological comorbidities.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".