Neurodevelopmental disorders in children seeking obesity treatment- associations with intellectual ability and psychiatric conditions
Bibliographic record
Abstract
Background Neurodevelopmental disorders (NDD), psychiatric comorbidity and cognitive deficits are commonly seen in children with obesity; however, little is known about the overlap between these conditions. This study aimed to examine the undiagnosed and diagnosed frequency of NDDs and explore its association with psychiatric conditions and general intellectual ability (IQ) in children presenting for obesity treatment. Methods In this observational study at two outpatient obesity clinics during 2018-2019, 80 children (8-17 years) were consecutively recruited, and screened for NDD unless already diagnosed with an NDD. A psychiatric unit evaluated children who screened positive for NDD. Diagnoses and clinical background factors were collected from medical records. IQ was assessed with the Weschler Intelligence Scales and internalizing symptoms were assessed using the Beck Youth Inventories. Associations between background factors, IQ and internalizing symptoms were explored in relation to having an NDD or not. Results We found that 47/80 children had at least one NDD. Children with a diagnosed NDD before study start (n = 30) had significantly more comorbidities than children diagnosed after the study screening (n = 17) (P = .01). Greater cognitive impairment was seen in children with NDD compared with children without an NDD (P = .01). Also, 33/73 participants self-reported substantial internalizing symptoms. At follow-up, 21/79 participants, in addition to NDD, had been diagnosed with another psychiatric disorder. Ten of these were children that had been diagnosed with an NDD before study start. Conclusion The overlap between NDD, cognitive deficits and psychiatric conditions in children with obesity is an important consideration for clinical practice. Screening for these conditions may be necessary when providing targeted interventions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".