Model systems inform rare disease diagnosis, therapeutic discovery and pre-clinical efficacy
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
Model systems have played a large role in understanding human diseases and are instrumental in taking basic research findings to the clinic; however, for rare diseases, model systems play an even larger role. Here, we outline how model organisms are crucial for confirming causal associations, understanding functional mechanisms and developing therapies for disease. As diseases that have been studied extensively through genetics and molecular biology, cystic fibrosis and Rett syndrome are portrayed as primary examples of how genetic diagnosis, model organism development and therapies have led to improved patient health. Considering which model to use, yeast, worms, flies, fish, mice or larger animals requires a careful evaluation of experimental genetic tools and gene pathway conservation. Recent advances in genome editing will aid in confirming diagnoses and developing model systems for rare disease. Genetic or chemical screening for disease suppression may reveal functional pathway members and provide candidate entry points for developing therapies. Model organisms may also be used in drug discovery and as preclinical models as a prelude to testing treatments in patient populations. Now, model organisms will increasingly be used as platforms for understanding variation in rare disease severity and onset, thereby informing therapeutic intervention.
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 | 0.000 | 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