Toward <scp>MRI</scp>‐based whole‐brain health assessment: The brain atrophy and lesion index (<scp>BALI</scp>)
Bibliographic record
Abstract
There have been many attempts to assess the elements of age- and dementia- related neurodegenerative changes in the brain using MRI; however, traditionally assessments focus only on single deficit. Over the past few years, our group has worked to create and validate the Brain Atrophy and Lesion Index (BALI) as an MRI-based whole-brain structural degeneration rating scale. The BALI can be used for applications in aging and dementia across the entire brain and can be applied to common clinical MR images. As a whole-brain structural health assessment, the BALI gives a more representative picture of how the brain ages. During the aging process, multiple elements of degeneration accumulate and interact to overwhelm repair processes and cause high-level failure in the function of the brain. To reflect this process, the BALI combines the assessment of several neurodegeneration changes into one scale. The BALI evaluation can be performed quickly and has been validated for use by non-neuroradiology expert raters trained with the method. This review gives a brief overview of the content of the BALI; covers the development, refinement, and application of the method; and provides insights about future development and clinical implementation of MRI-based whole-brain health assessment in aging and dementia.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".