Optimising and Future-Proofing Dementia Care With Amnestic Mild Cognitive Impairment (aMCI) Clinics
Bibliographic record
Abstract
Aims Amnestic Mild Cognitive Impairment (aMCI) is considered a pre-dementia (prodromal) phase of Alzheimer's disease (AD), with a higher probability in patients with positive biomarkers (temporo-parietal region, atrophy on CT/MRI imaging and hypometabolism on FDG-PET scan).We developed a pilot service development project in the North Sector of Gloucestershire Health and Care (GHC) Trust. Its’ main aim was to ease some of the pressures on the Memory Assessment Service (MAS) nurses and the medical memory clinics. The main objectives were: 1. To develop and run an aMCI Clinic service for eight months between March and November 2022 at GHC with North Sector patients to reduce waiting times compared to the preceding years. 2. In patients with aMCI and a positive biomarker, continue annual cognitive testing with early identification of conversion to dementia, thereby starting anti-dementia medication, and continue through the post-diagnosis pathway. Future plans include creating a business case for the Care Commission Group to consider commissioning a countywide aMCI service. Methods Patients (n=23) with the diagnosis of aMCI and a positive biomarker were selected. Data included the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) to assess patients’ daily functioning, clinical history and service satisfaction questionnaires. Different initial objective tests, including Addenbrookes Cognitive Examination (ACE-III), Repeatable Battery for the Assessment of Neuropsychological Status (R-BANS), Telephone Interview for Cognitive Status (TICS), and Rowland Universal Dementia Assessment Scale (RUDAS) were used. Data for waiting times from referral to first assessment were collected and statistically analysed using a repeated measures design across years 2020,2021,2022(March-November) and a one-way repeated measure ANOVA was performed. Results Analysis of waiting time indicated a non-significant decrease in waiting times from referral to first assessment. A decrease in the waiting times from September 2022-November 2022 was noted, pointing towards a possible time lag effect. Within six to twelve months of repeat testing, 62% of patients remained with an aMCI diagnosis whereas 32% of patients progressed to dementia (Alzheimer's or Vascular). From the post-appointment patient feedback received (65%), all patients reported to be very satisfied (57%) or satisfied (9%). Conclusion It is prudent to assess the time lag effect on the results produced in subsequent months. A repeat review with a larger sample size to increase the sensitivity and specificity of the results obtained is recommended.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".