O3‐06‐01: Chronic multimorbidity affects hippocampal volume in nondemented older individuals
Bibliographic record
Abstract
Most neuroimaging protocols that have assessed brain atrophy in normal aging have used stringent criteria to include only people free from diseases (e.g., hypertension, diabetes). However, epidemiological studies have shown that the majority of the elderly population is affected by multiple diseases. Thus, previous neuroimaging findings do not represent the general aging population. Here we aimed to study the effect of morbidity (one chronic disease) and multimorbidity (co-occurrence of 2 or more chronic disorders) on the hippocampus (HC), whose structural integrity is vulnerable to aging and pathological conditions including Alzheimer's disease. Study participants were derived from the Swedish National study of Aging and Care in Kungsholmen (SNAC-K). From the 555 non-demented individuals who underwent a magnetic resonance imaging protocol (MRI), 442 were retained after exclusion due to technical issues, brain pathology (e.g., stroke), and diseases directly related to the brain (e.g., Parkinson's disease). Participants with MMSE < 27 were also excluded. Subjects were stratified by age: 60, 66, 72, and 78+. HC volume was determined on T1-weighted MRIs, which were preprocessed in SPM8, using the voxel-based morphometry approach (segmentation, DARTEL spatial normalization with customized template creation, smoothing). The gray-matter images were further affine-aligned to standard Montreal Neurological Institute (MNI) space, and voxel-based analyses were computed within the hippocampal region-of-interest derived from the AAL-MNI atlas. We compared subjects with no disease, morbidity, and multimorbidity in the whole sample and within age groups. In the whole sample, controlling for age, gender, and education, HC volume was reduced when subjects had more than 2 diseases, whereas no effect was detected in the presence of only one disease. This global pattern held within age groups after 66 years old, and was most pronounced amongst the 66-year-olds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".