“Emplotted Narratives” and Structured “Behavioral Observations” Supporting the Diagnosis of Willis‐Ekbom Disease/Restless Legs Syndrome in Children with Neurodevelopmental Conditions
Bibliographic record
Abstract
BACKGROUND: Willis-Ekbom disease/restless legs syndrome (WED/RLS) seems to be a frequent cause of intractable chronic insomnia (ICI) but is under-recognized in children/adolescents with neurodevelopmental conditions (NDCs), as many patients do not have the ability to express the underlying "urge-to-move". In light of this, we aim to develop a protocol for behavioral observations supporting the diagnosis of WED/RLS. METHODS: We investigated 26 pediatric patients (age 1-16 years, median 8) with NDCs, ICI and evidence of familial WED/RLS employing (1) "emplotted narratives" for description of the various "urge-to-move" presentations and (2) self-description and "behavioral observations" during a "suggested clinical immobilization test" (SCIT). RESULTS: Parental narratives reflected typical WED/RLS-related "urge-to-move" symptoms during day-, bed-, and nighttime in all patients. Fifteen out of 26 patients could describe the "urge-to-move" during the SCIT. Ten out of 26 patients, unable to describe their symptoms due to cognitive disabilities, showed patterns of "relieving-movements" upon observation. Sensory processing abnormalities were reported in all patients, with tactile sensitivities (26/26) (including shifted pain threshold) as the most common sensory domain. CONCLUSION: "Emplotted narratives" and structured "behavioral observations" support recognition of familial WED/RLS associated movement patterns and provide a useful tool for the diagnosis of WED/RLS in children with NDCs in a clinical office setting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".