P1‐291: Early magnetic resonance imaging results from the addneuromed Alzheimer's disease study
Bibliographic record
Abstract
Multi-centre MRI studies are a key tool for investigating the natural history of mild cognitive impairment and Alzheimer's disease and for testing potential new pharmaceuticals. We present here early results from the first timepoint of the European Union and EFPIA sponsored InnoMed / AddNeuroMed multi-center MRI study of longitudinal changes in Alzheimer's disease (AD). MRI data compatible with the ADNI image acquisition protocol was collected from 85 AD patients, 87 subjects with MCI and 88 controls at six European MRI sites and uploaded to the Loris database system at the Karolinska Institutet, Sweden. The underlying database system was developed at the McGill Brain Imaging Centre, Montreal. Following careful quality control the 3D T1-weighted images were processed using the Civet image processing pipeline to automatically determine whole brain volumes normalized to the intracranial cavity (ICC) and mean cortical thickness measures. Right and left hippocampal volumes were determined by manual delineation by an expert observer and normalized to the ICC. Ninety-six percent of T1-weighted volumes passed the quality control criteria. Whole brain volumes, cortical thickness measures and hippocampal volumes showed significant differences between the Alzheimer's and MCI groups and between the Alzheimer's and control groups. Only hippocampal volumes showed significant difference between the control and MCI groups. The AddNeuroMed study has collected high quality data for 96% of the subjects enrolled across six different sites. Early results show the sensitivity of hippocampal volumes in distinguishing between Alzheimer's disease, mild cognitive impairment and control groups. These results will allow us to refine our strategy for further more detailed analyses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".