Bibliographic record
Abstract
Mild cognitive impairment (MCI) represents a transitional stage between healthy aging and dementia, and affects 10-15% of the population over the age of 65. The failure of drug trials in Alzheimer's disease (AD) treatment has shifted researchers' focus toward delaying progression from MCI to dementia, which would reduce the prevalence and costs of dementia profoundly. Diagnostic criteria for MCI increasingly emphasize the need for positive biomarkers to detect preclinical AD. The phenomenology of MCI comprises lower quality-of-life, greater symptoms of depression, and avoidant coping strategies including withdrawal from social engagement. Neurobiological features of MCI are hypoperfusion and hypometabolism in temporoparietal cortices, medial temporal lobe atrophy particularly in rhinal cortices, elevated tau and phosphorylated tau and decreased Aβ42 in cerebrospinal fluid, and brain Aβ42 deposition. Elevated tau can be identified in MCI, particularly in the entorhinal cortex, using positron emission tomography, and analysis of signal complexity using electroencephalography or magnetoencephalography holds promise as a biomarker. Assessment of MCI also relies on cognitive screening and neuropsychological assessment, but there is an urgent need for standardized cognitive tests to capitalize on recent discoveries in cognitive neuroscience that may lead to more sensitive measures of MCI. Cholinesterase inhibitors are frequently prescribed for MCI, despite the lack of evidence for their efficacy. Exercise and diet interventions hold promise for increasing reserve in MCI, and group psychoeducational programs teaching practical memory strategies appear effective. More work is needed to better understand the phenomenology and neurobiology of MCI, and how best to assess it and delay progression to dementia.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".