Common features of neurodegenerative disease: exploring the brain-eye connection and beyond (Part 1): the 2021 pre-symposium of the 15th international conference on Alzheimer’s and Parkinson’s diseases
Bibliographic record
Abstract
share similar mechanisms of neurodegeneration across multiple diseases.This meeting report (Part 1 of 2) summarizes two of the hot topics discussed in the AD/PD 2021 Common Features, including cellular senescence and resilience/resistance (visit the meeting website for a complete agenda https://adpd2021.kenes.com/commonfeatures-of-neurodegenerative-disease-exploring-thebrain-eye-connection-and-beyond).As funders of brain and eye diseases, BrightFocus supports research in each of these critical areas of disease pathology and anticipates that scientific discussions on these common elements will catalyze cross-disease collaborations and breakthroughs (information on how to apply for funding can be found at science.brightfocus.org/apply-grant).Since the topics and speakers change at every meeting, we look forward to your participation in the fourth installment of Common BrightFocus Foundation sponsored the third iteration of the "Common Features of Neurodegenerative Disease: Exploring the Brain-Eye Connection and Beyond" preconference symposium which has preceded the bi-annual International Conference on Alzheimer's and Parkinson's Diseases (AD/PD) for the past six years.Common Features highlights areas of disease pathology that Molecular Neurodegeneration
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.
How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".