Bibliographic record
Abstract
Despite promise in animal models, novel therapeutics consistently fail in clinical trials of autism. Due to the heterogeneous nature of autism, treatment will likely only ameliorate symptoms in a subset of patients. There is a need to simultaneously test promising new compounds while understanding and predicting which patients will respond to each therapy. To determine what might contribute to response susceptibility, the implementation of co-clinical trials in autism is proposed. Here, the implementation of multiple co-clinical trials is discussed, including how changes to the protocols were expanded and adapted as the projects proceeded. The first studies set the groundwork for the methodology, first in a study of high throughput characterization of behavioural and neuroanatomical measures in mouse models, then in a study of oxytocin in mouse models of autism. Next, the first true co-clinical trial of tideglusib was investigated in mouse models of autism, followed by an investigation of arbaclofen that deviated due to treatment side effects. A high-throughput protocol for mouse models of autism is discussed, which encompasses chronic treatment over development, multiple magnetic resonance imaging time-points, and behavioural tests before, during, and after treatment. Human subjects also follow an extensive phenotyping protocol, including behavioural testing, as well as genetics and imaging assessments. This multi-modal, multi-species approach is designed to be a rigorous method of assessing treatment effect, with the intention of stratification by response susceptibility. Considerations that are key to successful implementation of a co-clinical trial are discussed, including behavioural, neuroanatomical, treatment, subject, and data analysis aspects. Recommendations based off lessons-learned are also reviewed, including the design of a third co-clinical trial. All of these considerations are discussed and implemented in a manner that makes use of the heterogeneity of the disorder-- an aspect that historically is ignored-- while also prioritizing translation across species. The implementation of co-clinical trials is the right "next step" for autism research, where the classic progression of therapeutics has failed. The hope is to introduce the concept of co-clinical trials to the field of autism, creating a shift in our current approach to the treatment of the 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 | 0.060 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".