Dementia in Hungary: General practitioners’ routines and perspectives regarding early recognition
Bibliographic record
Abstract
Undetected dementia in primary care is a global problem. Since general practitioners (GPs) act as the first step in the identification process, examining their routines could help us to enhance the currently low recognition rates. The study aimed to explore, for the first time in Hungary, the dementia identification practices and views of GPs. In the context of an extensive, national survey (February-November 2014) 8% of all practicing GPs in Hungary (n = 402) filled in a self-administered questionnaire. The questions (single, multiple-choice, Likert-type) analysed in the present study explored GPs’ methods and views regarding dementia identification and their ideas about the optimal circumstances of case-finding. The vast majority of responding GPs (97%) agreed that the early recognition of dementia would enhance both the patients’ and their relatives’ well-being. When examining the possibility of dementia, most GPs (91%) relied on asking the patients general questions and only a quarter of them (24%) used formal tests, even though they were mostly satisfied with both the Clock Drawing Test (69%) and the Mini-Mental State Examination (65%). Longer consultation time was chosen as the most important facet of improvement needed for better identification of dementia in primary care (81%). Half of the GPs (49%) estimated dementia recognition rate to be lower than 30% in their practice. Hungarian GPs were aware of the benefits of early recognition, but the shortage of consultation time in primary care was found to be a major constraint on efficient case-finding.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".