Should older adults be screened for dementia? It is important to screen for evidence of dementia!
Bibliographic record
Abstract
Multiple arguments for considering routine dementia screening have been presented. Furthermore, dementia diagnoses are widely unrecognized. As a result, persons with dementia are missing important clinical care and treatment interventions. By distinction, the problems of defining, diagnosing, and treating mild cognitive impairment (MCI) are not yet resolved, and MCI is not ready for a screening recommendation. Dementia screening approaches, including cognitive testing and functional assessment, must be evaluated on their scientific merits, including sensitivity and specificity for recognizing affected individuals in at‐risk populations. Screening tests must be “cost‐worthy”, with the benefits of true‐positive test results justifying the costs of testing and resolving false‐positive cases, with due consideration for proper diagnostic evaluation and potential harms. With the tremendous number of new cases projected in the near future and the expected emergence of beneficial therapies, considerably more research is needed to develop more efficient screening systems. Editor's Note: This paper was written in response to a comment submitted to this Journal on the consensus statement by a group of scientists concerned about screening for dementia, which was published in this Journal in April 2006 [1]. The submitted manuscript was withdrawn after this response was submitted. However, this response is being published because it addresses concerns about screening recommendations and provides clarification and additional information on key points concerning dementia screening.
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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 | 0.013 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".