Cognitive Impairment in Healthy Filipino Adults with MRI Non-specific White Matter Change (P4.198)
Bibliographic record
Abstract
OBJECTIVE:To correlate non-specific white matter changes (NSWMC) with cognitive function in healthy Filipino adults with normal hippocampal volume and with no previous history of strokes. BACKGROUND: NSWMC are frequently seen in MRI of patients presenting with stroke or dementia, but are also observed to be present in healthy adults with no neurologic or cognitive impairment. NSWMC are sometimes considered mere radiological finding with uncertain clinical significance. DESIGN/METHODS:202 patients with MRI hippocampal volumetry, neurocognitive screening tests and normal metabolic parameters were studied. Hippocampal volume was determined using NeuroQuant®. Those with low hippocampal volume and MRI evidence of stroke of any age were excluded. MR images were reviewed and NSWMC were graded using Fazekas scale. Correlational statistical analysis was made on the relationship of NSWMC to neurocognitive scores. RESULTS:There is significant relationship between age and Fazekas score. The higher the age, the higher is the Fazekas score, and the greater is the non-specific white matter change (p=0.004). There is no significant correlation between Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MOCA) with the degree of NSWMC (p-value 0.06, 0.07). Worst confluent white matter change seen in Fazekas 4 did not correlate with worst MMSE or MOCA. Minimal WMC in Fazekas 0 did not equate to normal MMSE or MOCA. CONCLUSIONS:Our results varied from majority of large-scale studies on NSWMC worldwide. In other studies the population did not exclude subjects with small hippocampus or previous history of stroke. Our study aims is to correlate NSWMC to cognitive decline in an otherwise healthy adult with no vascular or Alzheimers dementia. We conclude that in healthy Filipino adults with normal hippocampal volume and with no history of vascular strokes, the presence of NSWMC does not correlate with cognitive impairment.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".