Review: some screening tests for dementia in older people are accurate and practical for use in primary care
Bibliographic record
Abstract
How accurate are screening tests for dementia in older people when used in primary care? Holsinger T, Deveau J, Boustani M, et al . Does this patient have dementia? JAMA 2007; 297 :2391–404. Clinical impact ratings Primary care ★★★★★★⋆ Internal medicine ★★★★★★⋆ Geriatrics ★★★★★★⋆ Neurology ★★★★★★⋆ Psychiatry ★★★★★⋆⋆ ### ![Graphic][1]</img>Data sources: Medline and PsycINFO (2000 to April 2006). Earlier studies were covered by a previously published review.* ### ![Graphic][2]</img>Study selection and assessment: English-language studies that evaluated screening tests for dementia, suitable for use by generalist physicians, in people >60 years of age without clinically obvious dementia. Included studies were required to use an acceptable criterion standard to diagnose dementia. Studies in patients in institutions or memory clinics or with <6 years of education and those … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".