Voices from the field: Expert reflections on mild cognitive impairment
Bibliographic record
Abstract
This special issue of Dementia is entitled ‘Voices from the Field: Expert Reflections on Mild Cognitive Impairment’. It consists of a nine interviews with some of the leading scientists, researchers and thinkers who have worked on Mild Cognitive Impairment, better known by its acronym ‘MCI’, within a constellation of issues ranging from its classification, diagnosis, aetiology, intervention, therapeutics, risks and ethics. We, Kevin R Peters (Department of Psychology) and Stephen Katz (Department of Sociology), are faculty members of Trent University in Peterborough, Canada. We conducted these interviews during 2012–13 as part of our ongoing project, ‘Perceptions and Realities of Mild Cognitive Impairment: Diagnosis and Treatment of Older Individuals’, funded by the Canadian Institutes of Health Research. We found that the experts, whom we interviewed, in their candid, informative and generous reflections on MCI and their work in the dementia field, produced as many questions as they did answers, especially regarding the validity of MCI as a disease category. Our discussions together expressed both the care and caution required to conceptualize the importance of MCI as a focal point for understanding predementia stages in relation to brain ageing. Today, as chronological age becomes less relevant as an age marker, other standards are emerging by which to segment the life course, and memory loss has become certainly one of them. Thus, cognitive health has joined physical health as a key indicator of successful ageing. In this context MCI has become an increasingly significant entity, yet there is still much uncertainty and lack of consensus about what it actually means. The common meaning amongst most neurologists, gerontologists, psychogeriatricians, care-workers and dementia researchers is that Mild Cognitive Impairment (MCI) is a category that indicates a measurable degree of cognitive impairment that does not meet the diagnostic criteria for dementia (e.g. Alzheimer disease or AD). Individuals with MCI are therefore not cognitively normal, but they are not demented either; they are somewhere in between these two ends of the spectrum of cognitive function. It is precisely this inbetween space, which this special issue of Dementia explores. MCI has been used in both
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".