Subiculum volumes associated with memory function in the oldest‐old individuals aged 95 years and older
Bibliographic record
Abstract
AIM: Few cohort studies targeting the oldest-old individuals have been carried out. The subiculum in the hippocampus is thought to be related to memory function, and atrophy of this structure might result in the conversion from amnestic mild cognitive impairment to Alzheimer's disease. Thus, we sought to examine the relationship between subiculum volumes and memory function in individuals aged ≥95 years, using a novel cognitive examination called the Addenbrooke's Cognitive Examination III (ACE-III) and an advanced magnetic resonance imaging analytical method, Multiple Automatically Generated Templates Brain Segmentation Algorithm (MAGeTbrain), to measure hippocampal subfield volumes. METHODS: A part of the cohort data of the Arakawa 95+ study for the oldest-old aged ≥95 years was used. A total of 10 individuals completed all of the examinations. The MAGeT brain was applied to estimate the subfield volumes of the hippocampus. Correlation analyses and multiple regression analyses were carried out to examine a relationship among ACE-III memory scores and the subfield volumes in the hippocampus, including the subiculum. RESULTS: = 0.46). CONCLUSIONS: The subiculum might play a pivotal role in memory function in the oldest-old individuals aged ≥95 years. The present finding warrants further research including larger sample sizes. Geriatr Gerontol Int 2019; 19: 347-351.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".