Increase in Psychoactive Drug Prescriptions in the Years Following Autism Spectrum Diagnosis: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Psychoactive medications are commonly prescribed to autistic individuals, but little is known about how their use changes after diagnosis. OBJECTIVES: This study describes the use of psychoactive drugs in children and young adults newly diagnosed with autism spectrum, between the year before and up to 5 years after diagnosis. METHODS: Multivariable logistic regression was used to examine the relationship between the use psychoactive drugs before the first diagnosis of autism spectrum condition (from 1998 to 2010), and the clinical and demographic characteristics, identified from public health care databases in Quebec. The types of drugs prescribed and psychoactive polypharmacy were evaluated over 5 years of follow-up. Generalized estimating equations (GEE) were used to examine the association of age and time with the use of psychoactive drugs. RESULTS: In our cohort of 2,989 individuals, diagnosis of another psychiatric disorder before autism spectrum strongly predicted psychoactive drug use. We observed that the proportion of users of psychoactive drugs increased from 35.6% the year before, to 53.2% 5 years after the autism spectrum diagnosis. Psychoactive polypharmacy (≥2 psychoactive drug classes) also increased from 9% to 22% in that time. Age and time since diagnosis strongly associated with the types and combinations of psychoactive drugs prescribed. CONCLUSIONS: Psychoactive drug use and polypharmacy increases substantially over time after autism spectrum disorder diagnosis in children.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".