Dementia Community Screening Program in District Health Area 11: Phase 1
Bibliographic record
Abstract
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".