P2‐369: The visual rating system for hippocampal atrophy: What does it assess?
Bibliographic record
Abstract
A promising visual rating system for hippocampal atrophy (rated 0-4) has been developed by Scheltens and shown to distinguish between normal aging and AD in numerous studies. The question of it's relation to volumetric measures has not been formally addressed. 5 individuals of varied levels of radiologic expertise (1 radiologist, 1 geriatrician, 1 Ph. D. student & 2 Research assistants) were trained in the use of the visual rating system and then individually assessed a sample of 122 scans selected from a larger cohort enrolled in a study of mild memory loss in the elderly (32 healthy elderly controls, 61 with a diagnosis of Mild Cognitive Impairment, and 29 with a diagnosis of Alzheimer's disease). These ratings were compared to measures of hippocampal volume, lateral ventricular volume, and atrophy ratio (the ratio of lateral ventricular volume to hippocampal volume). Ratings averaged across the group distinguished between the groups (Controls = 0.9, MCI = 1.63, AD = 2.35, p < 0.05). When correlated against volumetric measures, averaged ratings correlated better with the atrophy ratio measure (r = 0.76) than with the hippocampal volume (r = -0.50). All raters scored over r = .6 in their correlations, even those with limited previous expertise. These results suggest that the visual rating system can distinguish between diagnostic groups in a meaningful manner, and that the ratings reflect atrophy ratio more than hippocampal volume alone.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".