P4‐070: Confirmatory evidence of left/right asymmetry in Alzheimer's disease hippocampal atrophy using harmonized automated segmentation
Bibliographic record
Abstract
Hippocampal (HC) atrophy is an established diagnostic biomarker for Alzheimer's disease (AD). The Harmonized HC segmentation Protocol (HarP) has been established to increase segmentation accuracy by including substructures known to atrophy in AD, as well as reduce inter-study variability. The official release of the HarP segmentation labels provided an opportunity to study left-right asymmetry in HC atrophy with disease progression. Using our improved technique for patch-based segmentation we computed HC volumes automatically for the sample of 100 subjects released from the HarP project. The sample, taken from the ADNI dataset, was composed of 29 NC, 34 MCI and 37 probable AD subjects. Left/right anisotropy indices were calculated using the formula “Index = (HC left – HC right)/(HC left + HC right)”. Positive anisotropy implies left > right HC volumes, while negative anisotropy means right > left HC volumes Results (Figure 1) show a distinct progression of HC atrophy with both cognitive impairment progression and concurrent visual ratings of medial temporal lobe atrophy. Anisotropy was shown to increase as well with the reduction in volume, associated with both cognitive impairment and medial atrophy. The progression distinctly indicates a larger atrophy on the left side, reaching statistical significance in probable AD vs. controls (Table 1). In addition, no NC subject had an anisotropy index higher than 10% (Figure 2)
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".