The Mild Cognitive Impairment (MCI) in Searching for its Clinical Identity, Comment of the NEDICES Cohort Data
Bibliographic record
Abstract
Elderly cognitive decline is a well-known disorder that affects many people. One of the first medical definitions for this clinical reality was Kral’s “benign and malignant senescent forgetfulness”. After Kral, many authors proposed other entities of memory impairment or cognitive decline in the elderly: “Age-associated memory impairment”, “Agerelated memory decline”, “Ageing-associated cognitive decline”, Mild Cognitive Impairment (MCI) and “Cognitive Impairment Non-Dementia” (CIND) of the Canadian Study. And very recently, the DSM-V defined the elderly cognitive decline as a “Minor Neurocognitive Disorder”. By large, MCI had far more citations than any other predementia state in MEDLINE (more than 8,000 reviews in this medical database). This success in the medical literature is rather the expression of a controversy than a well-defined clinical disorder. In fact, its medical birth, near 30 years ago, was as a research entity that precludes dementia. The theoretical definition of MCI is quite clear (A cognitive decline with an increased dementia risk); the problem is the operational definition of this cognitive decline in many elderly that have produced, along the time many definitions and subtypes. In summary, MCI is defined as cognitive decline (of one or more cognitive domains, mainly memory) with normal or near normal functional activities of the patient, and obviously, no dementia. According to the type and extension of the affected cognitive domain, MCI has received several subtyping: Amnestic- only memory affected, non-amnestic- deficit in another cognitive domain different from the memory, such as executive capacities. Both of them could be shown alone or in combination (only amnestic MCI, only non-amnestic, or amnestic o non-amnestic plus other cognitive domain affected. There are several well-known characteristics of this entity. First, it is prevalent in the elderly, more prevalent (about 10-15%) that the dementia states (5-10%). Obviously, in both conditions, its prevalence oscillates with the operational definitions used and with the population demographic characteristics studied: age, sex and education. Second, MCI involves an increased risk of dementia and mortality in relation to the normal cognition elders. Third, it is an unstable disorder, many MCI cases do not evolve to dementia, and many others change to normal cognition in a period of 2-3 years. Fourth, MCI is a heterogeneous entity with many risk factors and aetiologies; it is not always the predementia state of the main neurodegenerative disorders of the elderly: Alzheimer disease (AD), Parkinson disease (PD) and others; cerebral vascular diseases, depression and elderly co-morbidities underpinning many MCI cases. From an epidemiological point of view, it is interesting to comment the MCI definition in three different scenarios: the clinical setting, the population-based surveys, and the trial studies because in these three scenarios, MCI had different characteristics and evolution.
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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 | 0.081 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".