Differences in diagnostic process, treatment and social Support for Alzheimer's dementia between primary and specialist care: resultss from the Swedish Dementia Registry
Bibliographic record
Abstract
Background: the increasing prevalence of Alzheimer's dementia (AD) has shifted the burden of management towards primary care (PC). Our aim is to compare diagnostic process and management of AD in PC and specialist care (SC). Design: cross-sectional study. Subjects: a total of, 9,625 patients diagnosed with AD registered 2011-14 in SveDem, the Swedish Dementia Registry. Methods: descriptive statistics are shown. Odds ratios are presented for test performance and treatment in PC compared to SC, adjusted for age, sex, Mini-Mental State Examination (MMSE) and number of medication. Results: a total of, 5,734 (60%) AD patients from SC and 3,891 (40%) from PC. In both, 64% of patients were women. PC patients were older (mean age 81 vs. 76; P < 0.001), had lower MMSE (median 21 vs. 22; P < 0.001) and more likely to receive home care (31% vs. 20%; P < 0.001) or day care (5% vs. 3%; P < 0.001). Fewer diagnostic tests were performed in PC and diagnostic time was shorter. Basic testing was less likely to be complete in PC. The greatest differences were found for neuroimaging (82% in PC vs. 98% in SC) and clock tests (84% vs. 93%). These differences remained statistically significant after adjusting for MMSE and demographic characteristics. PC patients received less antipsychotic medication and more anxiolytics and hypnotics, but there were no significant differences in use of cholinesterase inhibitors between PC and SC. Conclusion: primary and specialist AD patients differ in background characteristics, and this can influence diagnostic work-up and treatment. PC excels in restriction of antipsychotic use. Use of head CT and clock test in PC are areas for improvement in Sweden.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".