Effect of Propranolol on Word Fluency in Autism
Bibliographic record
Abstract
OBJECTIVE AND BACKGROUND: Autism is characterized by repetitive behaviors and impaired socialization and communication. Preliminary evidence showed possible language benefits in autism from the β-adrenergic antagonist propranolol. Earlier studies in other populations suggested propranolol might benefit performance on tasks involving a search of semantic and associative networks under certain conditions. Therefore, we wished to determine whether this benefit of propranolol includes an effect on semantic fluency in autism. METHODS: A sample of 14 high-functioning adolescent and adult participants with autism and 14 matched controls were given letter and category word fluency tasks on 2 separate testing sessions; 1 test was given 60 minutes after the administration of 40 mg propranolol orally, and 1 test was given after placebo, administered in a double-blinded, counterbalanced manner. RESULTS: Participants with autism were significantly impaired compared with controls on both fluency tasks. Propranolol significantly improved performance on category fluency, but not letter fluency among autism participants. No drug effect was observed among controls. Expected drug effects on heart rate and blood pressure were observed in both the groups. CONCLUSIONS: Results are consistent with a selective beneficial effect of propranolol on flexibility of access to semantic and associative networks in autism, with no observed effect on phonological networks. Further study will be necessary to understand potential clinical implications of this finding.
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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 | 0.001 |
| 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".