The AddNeuroMed framework for multi‐centre MRI assessment of Alzheimer's disease : experience from the first 24 months
Bibliographic record
Abstract
OBJECTIVE: To describe the AddNeuroMed imaging framework for multi-centre magnetic resonance imaging (MRI) assessment of longitudinal changes in Alzheimer's disease and report on early results from the first 24 months of the project. METHODS: A multi-centre study similarly to a faux clinical trial has been established to assess longitudinal MRI changes in Alzheimer disease (AD), mild cognitive impairment (MCI) and healthy control subjects using an image acquisition protocol compatible with Alzheimer disease neuroimaging initiative (ADNI). Comprehensive quality control (QC) measures have been established throughout the study. An intelligent web-accessible database holds details on both the raw images and data processed using a sophisticated image analysis pipeline. RESULTS: A total of 378 subjects have been recruited (130 AD, 131 MCI, 117 healthy controls) of which a high percentage (97.3%) of the T1-weighted volumes passed the QC criteria. Measurements of normalized whole brain volume and whole brain cortical thickness showed significant differences between AD and controls, AD and MCI and MCI and controls. CONCLUSIONS: A framework for multi-centre MRI studies of Alzheimer's disease has been established consisting of a harmonized MRI acquisition protocol across centres, rigorous QC at both the sites and central data analysis hub and an automated image analysis pipeline. Early results demonstrate the high quality of the images acquired and the applicability of the automated image analysis techniques employed.
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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 | 0.030 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".