Pharmacological treatment of disruptive behavior in Smith–Magenis syndrome
Bibliographic record
Abstract
Smith-Magenis syndrome (SMS) is a complex genetic syndrome caused by an interstitial deletion of chromosome 17p11.2. Children and adults with SMS appear to have unique neurobehavioral problems that include: sleep disturbance, self-injurious and maladaptive behaviors, stereotypies, and sensory integration disorders. We gathered retrospective psychotropic use information from parents or other caregivers of 62 individuals with SMS who were asked about use of psychotropic medication from a list of commonly used psychiatric medications. For those drugs identified, respondents were asked to rate the experience with the particular medication using a likert-type scale. Drugs were grouped into seven main categories: (1) stimulants; (2) antidepressants; (3) antipsychotics; (4) sleep aides; (5) mood stabilizers; (6) alpha 2 agonists; and (7) benzodiazepines. Relative frequencies, means and standard deviations pertaining to age and medication effect were derived for each medication category. Six of the seven medication categories examined showed no meaningful deviations from the "no change" score. The benzodiazepine group showed a mild detrimental effect. There were no gender differences in efficacy. Use of psychotropic medication started early in life (mean age 5 years), particularly with sleep aides. Although no medication category was identified as efficacious in SMS, all the categories reported herein may be considered as an option for brief symptomatic relief.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".