Systematic review of recent dementia practice guidelines
Bibliographic record
Abstract
BACKGROUND: dementia is a highly prevalent acquired cognitive disorder that interferes with activities of daily living, relationships and quality of life. Recognition and effective management strategies are necessary to provide comprehensive care for these patients and their families. High-quality clinical practice guidelines can improve the quality and consistency of care in all aspects of dementia diagnosis and management by clarifying interventions supported by sound evidence and by alerting clinicians to interventions without proven benefit. OBJECTIVE: we aimed to offer a synthesis of existing practice recommendations for the diagnosis and management of dementia, based upon moderate-to-high quality dementia guidelines. METHODS: we performed a systematic search in EMBASE and MEDLINE as well as the grey literature for guidelines produced between 2008 and 2013. RESULTS: thirty-nine retrieved practice guidelines were included for quality appraisal by the Appraisal of Guidelines Research and Evaluation II (AGREE-II) tool, performed by two independent reviewers. From the 12 moderate-to-high quality guidelines included, specific practice recommendations for the diagnosis and/or management of any aspect of dementia were extracted for comparison based upon the level of evidence and strength of recommendation. CONCLUSION: there was a general agreement between guidelines for many practice recommendations. However, direct comparisons between guidelines were challenging due to variations in grading schemes.
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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 | 0.029 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".