A pediatric patient with autism spectrum disorder and epilepsy using cannabinoid extracts as complementary therapy: a case report
Bibliographic record
Abstract
BACKGROUND: The pharmacological treatment for autism spectrum disorders is often poorly tolerated and has traditionally targeted associated conditions, with limited benefit for the core social deficits. We describe the novel use of a cannabidiol-based extract that incidentally improved core social deficits and overall functioning in a patient with autism spectrum disorder, at a lower dose than has been previously reported in autism spectrum disorder. CASE PRESENTATION: The parents of a 15-year-old boy, of South African descent, with autism spectrum disorder, selective mutism, anxiety, and controlled epilepsy, consulted a medical cannabis physician to trial cannabis extract to replace seizure medications. Incidentally, at a very low cannabidiol-based extract dose, he experienced unanticipated positive effects on behavioral symptoms and core social deficits. CONCLUSION: This case report provides evidence that a lower than previously reported dose of a phytocannabinoid in the form of a cannabidiol-based extract may be capable of aiding in autism spectrum disorder-related behavioral symptoms, core social communication abilities, and comorbid anxiety, sleep difficulties, and weight control. Further research is needed to elucidate the clinical role and underlying biological mechanisms of action of cannabidiol-based extract in patients with autism spectrum disorder.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".