Is Dementia Screening of Apparently Healthy Individuals Justified?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it