Granulovacuolar Degeneration in Hippocampus of Neurodegenerative Diseases: Quantitative Study
Bibliographic record
Abstract
Background. Granulovacuolar degeneration (GVD) is one of the pathological features long associated with Alzheimer’s disease (AD) and normal aging. Aim. We investigate the frequency of GVDs in AD, other neurodegenerative diseases, and normal aging, with attempt to determine whether the GVD has preponderance in any particular neurodegenerative disease other than AD. Materials and Methods. A retrospective review of 111 autopsied cases with a variety of neurodegenerative diseases and 70 control cases without pathological evidence of neurodegeneration was evaluated quantitatively. The microscopic examination was applied on coronal sections of hippocampi using Hematoxylin and Eosin (H&E) and Bielschowsky silver impregnation. The mean percentage of neurons with GVDs was calculated through all sectors of Ammon’s horn for each case. Result. We found that neurons with GVD, in cases with or without neurodegenerative diseases, were found predominantly in CA1 and CA2 sectors of hippocampus. The GVD count in AD was significantly increased in CA1 and CA2 compared to other neurodegenerative cases as well as normal aging controls. In AD/LBD there was a significant increase in GVD in CA1 whereas in LBD there was no significant change in GVD. Conclusions. The frequency of GVD in AD is due to the disease process and attributes the increase for AD/LBD to the AD component.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".