O1‐02–08: The innomed/addneuromed framework for multicenter MRI assessment of longitudinal changes in Alzheimer's disease
Bibliographic record
Abstract
The framework and first 24 months experience of the AddNeuroMed multi-center MRI study of longitudinal changes in Alzheimer's disease (AD) is described. AddNeuroMed is part of the European Union founded and EFPIA sponsored InnoMed programme and aims to develop and validate novel surrogate markers based upon in vitro and in vivo models in animals and humans, using AD as a test platform. The study duration is from September 2005 to January 2009. The imaging work package of AddNeuroMed aims to perform a multi center MRI study similar to a drug trial. This includes the installation of calibrated MRI pulse sequences, the acquisition of phantom scans, the establishment of routines for communication and data -flow, -collection, -storage, -backup, -quality control (QC), -quality assurance (QA), -evaluation and site feedback. Measurements from the MRI scans will be related to clinical and proteomics measures. MRI scans are collected at six different European MRI sites with a data collection and coordination center at Karolinska Institutet, Stockholm. Data management, workflow control and image QC are guided by the Loris database system (McGill Brain Imaging Centre, Montreal). The MRI acquisition protocol is the same as that employed by ADNI, with the addition of T1 and T2 relaxometry and MRS in a subset of patients. A total of 360 healthy control subjects, MCI patients or Alzheimer's disease patients will be scanned at baseline and 3 and 12 months thereafter. As of October 2007, 341 and 196 subjects have been scanned at baseline or at 3 month, respectively. All images have been databased and the baseline MRI data processed to produce measures of gray and white matter, CSF and hippocampal volumes together with cortical thickness. 97 % of all of the T1 volumes passed QC which demonstrates the excellent performance of the participating scanning sites. Hence, AddNeuroMed is successfully collecting data in a European multi-site MRI study. A database has been implemented and QC and QA is being performed on a routine basis.
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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 | 0.038 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.025 |
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".