Coexistence of Multiple Sclerosis and Alzheimer Disease Pathology: A Case Series
Bibliographic record
Abstract
Individuals with multiple sclerosis (MS) are now living close to normal lifespans and will likely suffer from the same diseases of aging as the general population. However, the coexistence of MS with diseases of aging remains poorly understood. In particular, little information exists describing the coexistence of MS with Alzheimer’s disease (AD), the most common form of dementia. In this case series, we searched a post-mortem pathological (autopsy) report database of the Vancouver General Hospital, Vancouver Coastal Health Authority in British Columbia, Canada to identify individuals with neuropathological features of both MS and AD. To complement the data from the autopsy reports, we accessed the medical records of the patients identified. Our search identified four individuals with pathological features of both MS and AD: three females and one male. Two individuals had pre-mortem diagnoses of MS while two did not. None of the patients with AD pathology had pre-mortem diagnoses of AD. In summary, this case series adds to the sparse literature describing the coexistence of these two relatively common neurological conditions and advances our understanding of the clinical and pathological features individuals with both MS and AD may present with. J Neurol Res. 2021;11(3-4):60-67 doi: https://doi.org/10.14740/jnr666
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".