Understanding the clinical care pathway of patients with mild cognitive impairment due to Alzheimer's disease and dementia due to Alzheimer's disease: Results from a global real‐world survey
Bibliographic record
Abstract
BACKGROUND: With the emergence of disease modifying treatments, the importance of early diagnosis of mild cognitive impairment (MCI) and mild dementia due to Alzheimer's disease (AD) has increased. This research explores the global clinical care pathway with the aim of highlighting areas for improvement for early and accurate diagnosis. METHOD: Data were drawn from the Adelphi Real World AD Disease Specific Programme™, a cross-sectional survey of physicians and their patients in Canada, France, Germany, Italy, Spain, the United Kingdom, Japan, and the United States between December 2022 - March 2024. Physicians reported data on patient's diagnostic pathway, Mini-Mental State Examination (MMSE) score at first consultation, and treatment for approximately their next nine consulting patients diagnosed with MCI due to AD or dementia due to AD (clinically diagnosed or biomarker confirmed). Patients self-reported their reasons for delaying consultation. Data was grouped by MMSE. Analyses were descriptive. RESULT: Overall, 829 physicians reported data for 5654 patients; 726 self-completed forms. Patient mean (standard deviation) age was 76.7 (8.4) years and 48.0% were male. Of those with an MMSE score at first consultation, 20.6% had 26-30, 47.2% 21-25, 29.9% 11-20, and 2.3% 0-10. Time from symptom onset to first consultation was a median [interquartile range] of 20.7 [4.6, 52.0] weeks. Patients reported delaying first visiting a physician (75.1%), mainly due to believing their memory problems were a part of normal ageing (72.3%). Most patients first consulted a primary care physician (73.4% of patients with an MMSE of 26-30; 58.7% 0-10), of which 72.4% and 45.5%, respectively, were referred on for diagnosis. Key diagnostic tools were behavioural/cognitive assessments (91.8%), feedback from patient/patient's family (89.3%), and non-AD specific blood tests (88.7%). Biomarker testing was infrequent (11.8%). When treatment was prescribed, lack of efficacy was the key driver for change in initial treatment (48.9%). CONCLUSION: Our results highlight a lack of awareness of early symptoms, inconsistent referral, and infrequent utilization of AD specific biomarkers. Addressing these challenges is pivotal to facilitate early, accurate diagnosis and intervention. Lack of efficacy was the main reason for changing initial treatment, indicating the need for better treatment options.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".