Diagnostic journey and barriers to diagnosis for patients with mild cognitive impairment or dementia due to Alzheimer’s disease in Canada: results from a real‐world survey
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) dementia progresses from preclinical brain changes, through mild cognitive impairment (MCI), to AD with dementia. Early diagnosis and confirmation of underlying AD pathology is crucial; however, there is still much to learn about patients’ diagnostic journey. We aimed to describe the diagnostic journey and barriers to diagnosis for patients with MCI or dementia due to AD in Canada. Method Data were collected from the Adelphi Real World AD Disease Specific Programme (DSP)™, a cross‐sectional survey of general practitioners (GPs) and specialists in Canada, from March to October 2023. GP surveys covered patient management and referral patterns. Specialists completed a survey capturing attitudes towards diagnosis, advanced testing, and future treatment landscape. Physicians saw ≥5 (GPs) or ≥10 (specialists) patients per week with MCI or dementia/AD. Analyses are descriptive. Result The survey was completed by 20 GPs and 30 specialists (19 neurologists, 6 psychiatrists, and 5 geriatricians). GPs reported patient difficulty remembering people’s names (65%) was the patient complaint that most often prompted further testing (Table 1). Other important patient complaints were problems concentrating on everyday tasks (45%) and worry about forgetting things, such as appointments so they have to rely on notes and calendars (45%). GPs reported referring an average of 25±22% of patients with MCI to a specialist after seeing them an average of 3.9±1.3 times. On average, 49±28% of patients with MCI or dementia/AD managed by GPs were not referred to a specialist. Specialists reported the top three barriers to early identification of patients with MCI and patients with mild dementia due to AD were delay due to lack of awareness of the condition (67% and 53%), lack of understanding what constitutes “normal” ageing (63% and 40%), and slow referral (37% and 40%) (Figure 1). Conclusion Many patients with MCI or dementia/AD in Canada are not referred by GPs, especially in early‐stage AD. Referrals from GPs were also often delayed, with specialists citing delay due to lack of patient awareness of MCI as the main barrier to early diagnosis. Improving awareness of early AD symptoms and AD pathology, and accelerating access to specialists, are warranted.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".