P3‐359: ASSESSMENT OF WHOLE‐BRAIN STRUCTURAL CHANGES USING THE BRAIN ATROPHY AND LESION INDEX: A VALIDATION ACROSS MULTIPLE INDEPENDENT DATASETS
Bibliographic record
Abstract
The brain is a complex and interconnected system; multiple neurodegenerative changes can cumulate to contribute to cognitive decline. The MRI based Brain Atrophy and Lesion Index (BALI) has been created to evaluate structural changes of the whole brain by summarizing deficits in several categories. So far, BALI has been applied to thousands of subjects from multiple independent datasets from around the world. In the present study, we examined the consistency of the BALI assessment in differentiating subjects with Alzheimer's disease, mild cognitive impairment, and normal cognition, and the associations of BALI with age and cognition. Data were obtained from studies published between 2010 and 2017, which involved large-scale open-access multi-centre datasets (n=2790), as well as several local datasets (n=310). Evaluation of the MR images followed the standard BALI assessment schema and carried out by multiple raters trained on the method. Results from the studies were compared and then pooled. Differences in the mean BALI scores across diagnoses were investigated using the Wallis-Kruskal Chi2 test and Cohen effect size. Inter-rater agreement rate was consistently high, ranging from 0.81 to 0.91. In all datasets, there was a difference in the BALI total score and sub-categorical scores between diagnostic groups (e.g. total BALI, Chi2>24.0, p<0.001) with variations between studies. Analyses combining the samples suggested a medium to large effect size depending on diagnosis. The associations between the total BALI score and age (p<0.0001) and various measures of cognition (p<0.01) were also consistent. The BALI was applied to a series of datasets for assessment of whole-brain structural changes in ageing-dementia, showing as both robust and generalizable. The method allows us to investigate the impact of aging on brain structural health, and assess how this affects cognitive decline and dementia. Variations in BALI assessment between different studies can be explained by differences in the study samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".