P.094 The three sisters of fate: Genetics, pathophysiology and outcomes of animal models of neurodegenerative diseases
Bibliographic record
Abstract
Background: Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease are neurodegenerative disorders characterized by progressive structural and functional loss of specific neuronal populations, protein aggregation, insidious adult onset, and chronic progression. Modeling these diseases in animal models is useful for studying the relationship between neuronal dysfunction and abnormal behaviours and for screening therapies. Methods: We conducted a comprehensive descriptive review of the numerous animal models currently available to study these three diseases with a focus on their utilities and limitations. Results: A vast range of genetic and toxin-induced models have been generated. Our review outlines how these models differ with regards to the genetic manipulation or toxin used and the brain regions lesioned, describes the extent to which they mimic the neuropathological and behavioral deficits seen in the human conditions, and discusses the advantages and drawbacks of each model. Conclusions: We recommend the adoption of a conservative approach when extrapolating findings based on a single animal model and the validation of findings using multiple models. Investing in additional preclinical studies before embarking on more expensive human trials will improve our understanding of the neuropathology underlying neuronal demise and enhance the chances of identifying effective therapies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".