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Record W3111070165 · doi:10.1002/alz.039419

Assessing and improving primary care physician confidence in the recognition of young‐onset and atypical dementia: A pilot intervention

2020· article· en· W3111070165 on OpenAlexaff
Hui Jin Chiew, Tanya‐Marie Yuen Oi Choong, Esther Vanessa Chua, Nyu Mei Mei, Linda Lay Hoon Lim, Kok Pin Ng, Adeline Su Lyn Ng, Simon Kang Seng Ting, Shahul Hameed, Nagaendran Kandiah

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsDementiaReferralMedicineIntervention (counseling)CognitionMemory clinicDiseasePsychiatryFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.054
GPT teacher head0.324
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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