Brain collection, standardized neuropathologic assessment, and comorbidity in Alzheimer's Disease Neuroimaging Initiative 2 participants
Bibliographic record
Abstract
INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative Neuropathology Core (ADNI-NPC) facilitates brain donation, ensures standardized neuropathologic assessments, and maintains a tissue resource for research. METHODS: The ADNI-NPC coordinates with performance sites to promote autopsy consent, facilitate tissue collection and autopsy administration, and arrange sample delivery to the NPC, for assessment using National Institute on Aging-Alzheimer's Association neuropathologic diagnostic criteria. RESULTS: The ADNI-NPC has obtained 45 participant specimens, and neuropathologic assessments have been completed in 36 to date. Challenges in obtaining consent at some sites have limited the voluntary autopsy rate to 58%. Among assessed cases, clinical diagnostic accuracy for Alzheimer disease (AD) is 97%; however, 58% of cases show neuropathologic comorbidities. DISCUSSION: Challenges facing autopsy consent and coordination are largely resource related. The neuropathologic assessments indicate that ADNI's clinical diagnostic accuracy for AD is high; however, many AD cases have comorbidities that may impact the clinical presentation, course, and imaging and biomarker results. These neuropathologic data permit multimodal and genetic studies of these comorbidities to improve diagnosis and provide etiologic insights.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".