Identification of Potential or Preclinical Cognitive Impairment and the Implications of Sophisticated Screening with Biomarkers and Cognitive Testing: Does It Really Matter?
Bibliographic record
Abstract
The last decade has seen an enormous growth in the interest in the recognition of and intervention in those diagnosed and living with the whole range of cognitive impairment and frank dementia. In the western world, the recognition of the impact on patients, families, health care systems, and societies that dementia poses has led to great efforts to help define the indicators for current and future dementia with the intention to treat those already afflicted even with the primarily symptomatic medications that exist and to recognize those at future risk with the hope of providing counselling to forestall its future development. The idea of "early diagnosis" appears at first glance to be attractive for the purposes of future planning and research studies, but it is not clear what the benefits and risks might be if screening processes define people at risk when beneficial interventions might not yet be determined. The ethical as well as financial implications must be explored and defined before implementation of such screening becomes a normal standard of practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".