Assessing and improving primary care physician confidence in the recognition of young‐onset and atypical dementia: A pilot intervention
Bibliographic record
Abstract
Abstract Background Young‐onset dementia (YOD) is defined as dementia with symptom onset below 65 years. The heterogeneous symptomatology of YOD can lead to delayed recognition and referral by primary care physicians (PCPs). Data from our tertiary referral centre in Singapore show that atypical presentations of YOD are more likely be referred and diagnosed at a later stage of disease than typical amnestic Alzheimer disease (AD) dementia. Method We piloted a structured, multimodal training programme to assess and increase the confidence of PCPs in the recognition of YOD and atypical dementia. The programme combined lectures, case studies and interactive workshops in 4 half‐day modules conducted between September 2019‐January 2020 (Module 1: clinical approach to cognitive impairment, overview of cognitive disorders and AD; Module 2: YOD, atypical and reversible causes of dementia; Module 3: cognitive assessment tools; Module 4: management of dementia, with a focus on YOD). Faculty included 6 cognitive neurologists and 4 dementia care nurses. 28 PCPs involved in dementia care in primary care memory clinics from all regions of Singapore were enrolled by invitation in mid‐2019. Prior to module 1, PCPs completed an anonymized questionnaire with 15 questions assessing their confidence in the recognition, diagnosis and management of YOD on a 5‐point Likert scale. An identical follow‐up questionnaire was administered upon completion of the programme. Results 20 baseline and 20 follow‐up questionnaire responses were obtained. The PCPs had a mean of 1.93 (SD 2.10) years of dementia care experience. At baseline, they were significantly more confident in recognizing symptoms of typical elder‐onset AD (70% confident, 10% very confident) compared to YOD (30% confident) or atypical/non‐AD dementia (15% confident), (p<0.001). Upon programme completion, PCPs reported significantly increased confidence in recognition of YOD (70% confident, 15% very confident; p<0.001) and atypical/non‐AD dementia (75% confident, 10% very confident; p<0.001). These findings persisted even when stratified by years of dementia care experience. Conclusion A structured, multimodal training programme was effective in improving PCP confidence in the recognition of YOD and atypical dementia. Follow‐up assessment of PCP knowledge and longitudinal monitoring of referral trends will help to determine its long‐term efficacy.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".