Iron Deficiency and Restless Sleep/Wake Behaviors in Neurodevelopmental Disorders and Mental Health Conditions
Bibliographic record
Abstract
Iron deficiency (ID) and restlessness are associated with sleep/wake-disorders (e.g., restless legs syndrome (RLS)) and neurodevelopmental disorders (attention deficit/hyperactivity and autism spectrum disorders (ADHD; ASD)). However, a standardized approach to assessing ID and restlessness is missing. We reviewed iron status and family sleep/ID history data collected at a sleep/wake behavior clinic under a quality improvement/quality assurance project. Restlessness was explored through patient and parental narratives and a ‘suggested clinical immobilization test’. Of 199 patients, 94% had ID, with 43% having a family history of ID. ADHD (46%) and ASD (45%) were common conditions, along with chronic insomnia (61%), sleep-disordered breathing (50%), and parasomnias (22%). In unadjusted analysis, a family history of ID increased the odds (95% CI) of familial RLS (OR: 5.98, p = 0.0002, [2.35–15.2]), insomnia/DIMS (OR: 3.44, p = 0.0084, [1.37–8.64]), and RLS (OR: 7.00, p = 0.01, [1.49–32.93]) in patients with ADHD, and of insomnia/DIMS (OR: 4.77, p = 0.0014, [1.82–12.5]), RLS/PLMS (OR: 5.83, p = 0.009, [1.54–22.1]), RLS (OR: 4.05, p = 0.01, [1.33–12.3]), and familial RLS (OR: 2.82, p = 0.02, [1.17–6.81]) in patients with ASD. ID and restlessness were characteristics of ADHD and ASD, and a family history of ID increased the risk of sleep/wake-disorders. These findings highlight the need to integrate comprehensive blood work and family history to capture ID in children and adolescents with restless behaviors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".