Mild cognitive impairment: a concept in evolution
Bibliographic record
Abstract
The construct of mild cognitive impairment (MCI) has evolved over the past 10 years since the publication of the new MCI definition at the Key Symposium in 2003, but the core criteria have remained unchanged. The construct has been extensively used worldwide, both in clinical and in research settings, to define the grey area between intact cognitive functioning and clinical dementia. A rich set of data regarding occurrence, risk factors and progression of MCI has been generated. Discrepancies between studies can be mostly explained by differences in the operationalization of the criteria, differences in the setting where the criteria have been applied, selection of subjects and length of follow-up in longitudinal studies. Major controversial issues that remain to be further explored are algorithmic versus clinical classification, reliability of clinical judgment, temporal changes in cognitive performances and predictivity of putative biomarkers. Some suggestions to further develop the MCI construct include the tailoring of the clinical criteria to specific populations and to specific contexts. The addition of biomarkers to the clinical phenotypes is promising but requires deeper investigation. Translation of findings from the specialty clinic to the population setting, although challenging, will enhance uniformity of outcomes. More longitudinal population-based studies on cognitive ageing and MCI need to be performed to clarify all these issues.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| 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".