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Record W3141067080 · doi:10.1017/cjn.2019.103

P.001 Rate of cognitive decline in dementias in patients from rural and remote Saskatchewan

2019· article· en· W3141067080 on OpenAlexaffvenueabout
IU Shahab, Andrew Kirk, Chandima Karunanayake, Megan E. O’Connell, D. Morgan

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsSaskatoon Medical Imaging
Fundersnot available
KeywordsDementiaDementia with Lewy bodiesVascular dementiaFrontotemporal dementiaMedicineAlzheimer's diseaseCognitive declineDiseasePsychiatryPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Background: To determine whether there is a difference in the average annual rate of decline in Mini Mental Status Examination (MMSE) scores between those with Alzheimer’s disease, vascular dementia, frontotemporal dementia and dementia with Lewy bodies. Methods: We conducted a retrospective chart review of 225 consecutive patients with dementia who attended the Rural and Remote Memory Clinic in Saskatoon, Saskatchewan. The data collected included MMSE scores and demographic information. Statistical analysis with ANOVA compared the average the annual rate of decline in MMSE score between patients with different types of dementia. Results: There was no statistically significant difference in the rate of MMSE score decline between these groups. Patients with frontotemporal dementia and vascular dementia were referred to the clinic at younger ages than those with Alzheimer’s disease and dementia with Lewy bodies. Conclusions: The rate of decline in MMSE did not differ between these four types of dementia. Patients with frontotemporal dementia and vascular dementia often experience cognitive decline earlier in life than those with Alzheimer’s disease and dementia with Lewy bodies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.815
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.287
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes3
Has abstractyes

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