Preclinical research in Rett syndrome: setting the foundation for translational success
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In September of 2011, the National Institute of Neurological Disorders and Stroke (NINDS), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the International Rett Syndrome Foundation (IRSF) and the Rett Syndrome Research Trust (RSRT) convened a workshop involving a broad cross-section of basic scientists, clinicians and representatives from the National Institutes of Health (NIH), the US Food and Drug Administration (FDA), the pharmaceutical industry and private foundations to assess the state of the art in animal studies of Rett syndrome (RTT). The aim of the workshop was to identify crucial knowledge gaps and to suggest scientific priorities and best practices for the use of animal models in preclinical evaluation of potential new RTT therapeutics. This review summarizes outcomes from the workshop and extensive follow-up discussions among participants, and includes: (1) a comprehensive summary of the physiological and behavioral phenotypes of RTT mouse models to date, and areas in which further phenotypic analyses are required to enhance the utility of these models for translational studies; (2) discussion of the impact of genetic differences among mouse models, and methodological differences among laboratories, on the expression and analysis, respectively, of phenotypic traits; and (3) definitions of the standards that the community of RTT researchers can implement for rigorous preclinical study design and transparent reporting to ensure that decisions to initiate costly clinical trials are grounded in reliable preclinical data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it