Prevalence of Geriatric Syndromes and Satisfaction of Service in Older People Receiving Annual Health Screening at the Check-up Clinic, Siriraj Hospital
Bibliographic record
Abstract
Objective: To assess the prevalence of geriatric syndromes (GSs) and evaluate service satisfaction among older patients attending a comprehensive geriatric check-up clinic (CGCC) at Siriraj Hospital. Materials and Methods: A cross-sectional study was conducted at the CGCC from December 2021 to November 2022. Participants aged 60 years and older were screened using a two-step approach; short screening by a standard questionnaire followed by an in-depth assessment. GSs were identified through standardized tools, and patient satisfaction was assessed using a structured questionnaire. Results: Of 159 participants, 67.9% were women, with a mean age of 66.6 ± 6 years. In step 1, 43.7% showed cognitive impairment, 29.1% had a risk of falls, and 76% reported oral health issues. Step 2 confirmed cognitive impairment in 40.6%, malnutrition risk in 93.3%, and sarcopenia in 78%. The mean satisfaction score was 33.3 ± 2.5 out of 35 points. Total service time averaged 104.2 ± 36.9 minutes. Conclusion: In a health check-up clinic where GSs are often under-recognized, GSs, particularly cognitive impairment, fall risk, and oral health issues, are prevalent. Despite extended service times, patient satisfaction remained high, highlighting the importance of comprehensive screening in geriatric care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".