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Record W4235087050 · doi:10.14740/jnr666

Coexistence of Multiple Sclerosis and Alzheimer Disease Pathology: A Case Series

2021· article· en· W4235087050 on OpenAlexaffvenueabout
Pauline Luczynski, Cornelia Laule, Ging‐Yuek Robin Hsiung, G. R. Wayne Moore, Helen Tremlett

Bibliographic record

VenueJournal of Neurology Research · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of British ColumbiaIsland Health
Fundersnot available
KeywordsAutopsyPathologicalMedicineDiseaseMultiple sclerosisDementiaMedical diagnosisPopulationPathologyPsychiatry

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.263
GPT teacher head0.411
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes3
Has abstractyes

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