Bibliographic record
Abstract
News BriefsBlood test for Alzheimer's MUHC study identifies procedure that detects early stages Montreal May 4, 2011 -A new blood test that will diagnose Alzheimer's disease may soon hit the market, thanks to an innovative study from the Research Institute of the McGill University Health Centre (MUHC).Their findings have characterized a unique biochemical diagnosis, which identifies patients with this devastating disorder.This research, published in the month's issue of the Journal of Alzheimer's Disease, has implications for the half-a-million Canadian sufferers and many millions more worldwide.''Until now, there has been no definitive diagnostic tool for Alzheimer's, other than postmortem analysis of brain tissue,'' says senior author Dr. Vassilios Papadopoulos, director of the MUHC Research Institute.''Our clinical study shows that a non-invasive blood test, based on a biochemical process, may be successfully used to diagnose Alzheimer's at an early stage and differentiate it from other types of dementia.''
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.173 | 0.133 |
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".