Reliability of the <scp>MRI</scp>‐based Brain Atrophy and Lesion Index in the evaluation of whole‐brain structural health
Bibliographic record
Abstract
Abstract Background The Brain Atrophy and Lesion Index (BALI), which evaluates several common aging‐related MRI changes in combination, has been validated as a feasible method to assess the status of structural brain health. Previous studies have been based primarily on older participants and high‐field MRI. Here, we tested the generalizability of the BALI by examining its measurement properties in a wide age range at both high and conventional MRI field strengths. Methods Subjects (n = 229) who had T2WI at either 1.5T or 3.0T were grouped into younger (age ≤ 60 years) and older (age > 60 years) groups. Image evaluation and scoring were performed independently by two experienced neuroradiologists who have mastered the BALI method. Inter‐ and intrarater agreement rates were examined comparing age groups and field strengths. Results The intraclass correlation coefficient for the BALI total score was consistently high under each experimental condition (interrater ICC ≥ 0.92, 95% CI: 0.84‐0.96), with no statistical difference between age groups (Fisher Z = 1.43) or field strengths (Z = 0.60). The reliability for BALI category subscores ranged between moderate and perfect (eg, 0.85 vs 0.57 for GA), similar for both age groups and typically greater at 3.0T than at 1.5T. Conclusion The BALI based on T2WI can be reliably applied to the evaluation of the whole‐brain health of both younger and older adults at both field strengths, even though high‐field MRI is preferable.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".