Bibliographic record
Abstract
PURPOSE OF REVIEW: Up to 35% of children and youth with autism spectrum disorder (ASD) receive at least one psychotropic medication. 50-70% of this population also receives biologically based complementary and alternative medicine (CAM). The data evaluating such practices are being reviewed. RECENT FINDINGS: There are accumulating data to suggest that atypical antipsychotics and stimulants may be useful for the treatment of irritability and hyperactivity in children and youth with ASD. The data for the use of selective serotonin reuptake inhibitors are less promising. New avenues of pharmacologic research targeting molecular targets identified by genomics, animal models and neuropathology are being evaluated. Areas of interest include glutamate/gamma-aminobutyric acid systems, neuropeptides such as oxytocin, and immune dysfunction, among others. In the case of biologically based CAM, a few compounds have been shown to be well tolerated, although efficacy is still being evaluated, such as melatonin, certain vitamins, and omega 3 fatty acids. Others have safety concerns without demonstrated efficacy, such as chelation therapies. SUMMARY: Accumulating data suggest a series of existing medications may be useful in ASD and large randomized clinical trials are necessary to evaluate safety and efficacy of both pharmaceuticals and alternative treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".