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Record W3044441261 · doi:10.48048/wjst.2020.5741

Dementia Community Screening Program in District Health Area 11: Phase 1

2020· article· en· W3044441261 on OpenAlexaboutno aff
Tharin Phenwan, Weeratian Tawanwongsri, Phanit Koomhin, Udomsak Saengow

Bibliographic record

VenueWalailak Journal of Science and Technology (WJST) · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDementiaMontreal Cognitive AssessmentDyslipidemiaCognitive impairmentGerontologyCommunity healthPhysical therapyInternal medicinePsychiatryCognitionPublic healthObesityDiseaseNursing

Abstract

fetched live from OpenAlex

To estimate the prevalence of dementia among Thai elderly in the upper Southern region of Thailand, we performed a cross-sectional screening of all Thai older people from 2 areas of Nakhon Si Thammarat province: Tambon Baan Thungchon, Tha Sala district, and Moo 6 and 7 from Sichon district, from December 2016 to November 2017. Trained health volunteers identified the participants in their communities and collected data including age, gender, comorbidities, Timed Up and Go Test (TUGT) results, and Montreal Cognitive Assessment (MoCA) scores. Our sample comprised 773 participants, of which 605 (78.3 %) were from Baan Thungchon area, while 168 were from Moo 6 and Moo 7 of Sichon district. The majority of participants were female (431, 55.7 %). The mean age of the participants was 79 ± 9.1 years, with a minimum age of 60, and a maximum age of 95. Their comorbidities were hypertension (42.9 %), type II diabetic mellitus (33.2 %), dyslipidemia (37.5 %), and osteoarthritis of the knees (35.8 %). 35.1 % of them also had positive TUGT. Sixty-seven participants (8.7 %) scored 7 or lower in the Abbreviated Mental Test (AMT). Five participants (7.5 %) had a positive screening for dementia.

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)0.001
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 score0.316
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.052
GPT teacher head0.378
Teacher spread0.326 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueWalailak Journal of Science and Technology (WJST)Same topicDementia and Cognitive Impairment ResearchFrench-language works237,207