Dementia with Lewy bodies. Review of diagnosis and pharmacologic management.
Bibliographic record
Abstract
OBJECTIVE: To review clinical features of dementia with Lewy bodies (DLB) and to guide family physicians in pharmacologic management, including medications to avoid. QUALITY OF EVIDENCE A MEDLINE: search of literature from 1995 to 2002 used the MeSH terms dementia with Lewy bodies/diagnosis, dementia with Lewy bodies/therapy, and antipsychotics/dementia with Lewy bodies. Level II and III evidence was available for diagnosis and treatment of DLB. One randomized controlled trial of rivastigmine was reviewed and appraised. MAIN MESSAGE: Dementia with Lewy bodies is common. Diagnosis can be made by family physicians using clinical criteria including presence of dementia with marked fluctuation in performance, hallucinations, and the onset of parkinsonism. Cholinesterase inhibitors should be considered for neuropsychiatric symptoms. Levodopa-carbidopa combinations should be considered for treatment of parkinsonism. Neuroleptics should be used with caution because of the risk of serious sensitivity reactions. If they are needed, atypical agents could be safer. CONCLUSION: Recognition and diagnosis of DLB is important to optimize pharmacologic management and to minimize risk of adverse reactions to neuroleptics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".