Measuring Effectiveness of Treatments in Rare Disorders, Evidence from Clinical Trials in Fragile X Syndrome
Bibliographic record
Abstract
Abstract Fragile X Syndrome (FXS) is the leading cause of inherited intellectual disability and autism spectrum disorder (ASD). It results from mutations of the FMR1 gene and the subsequent loss of Fragile X mental disorder 1 protein (FMRP). FXS has been the focus of intense preclinical research leading to several clinical trials. Effect sizes compare the effectiveness of treatments for neurodevelopmental disorders, such as FXS. This paper reviews effect sizes of different drug treatments in clinical trials for FXS to understand potential issues with the designs of current trials which could affect efficacy detectability. We searched for "Fragile X Syndrome" query in PubMed and filtered the clinical trials and downloaded the list of all publication IDs. PubMed's API was then utilized to collect the abstract's content into a spreadsheet. Studies were narrowed down to those with sufficient statistics data to calculate effect size relative to placebo. Phase 2 and 3 clinical trials for arbaclofen and one trial for L-acetylcarnitine reported large effect sizes. No significant findings were present between placebo and treatment groups. Studies without a placebo group reported larger treatment effect sizes. Small sample sizes, scarcity of studies, and outcome measures based on caregiver reports prevented us from drawing conclusions.
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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 | 0.169 | 0.466 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".