Is Dementia Screening of Apparently Healthy Individuals Justified?
Bibliographic record
Abstract
Despite efforts to raise awareness and develop guidelines for care of individuals with dementia, reports of poor detection and inadequate management persist. This has led to a call for more identification of people with dementia, that is, screening individuals who may or may not complain of symptoms of dementia in both acute settings and primary care. The following should be considered before recommending screening for dementia among individuals in the general population. Dementia Tests. Low prevalence reduces positive predictive value of tests and screening tests will miss people who have dementia and identify people who do not have dementia in substantial numbers. Clinical Issues. The clinical course of dementia has not yet been shown to be amenable to intervention. Misdiagnosis and overdiagnosis can have significant long-term effects including stigmatization, loss of employment, and autonomy. Economic Issues. Health systems do not have the capacity to respond to increased demand resulting from screening. In conclusion, at present attention to life-course risk reduction and support in the community for frail and cognitively impaired older adults is a better use of limited healthcare resources than introduction of unevaluated dementia screening programs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".