Feasibility of remote Memory Clinics using the plan, do, study, act (PDSA) cycle
Bibliographic record
Abstract
INTRODUCTION: A timely diagnosis of dementia is crucial for initiating and maintaining support for people living with dementia. The coronavirus disease (COVID) pandemic temporarily halted Memory Clinics, where this is organised, and rate of dementia diagnosis has fallen. Despite increasing use of alternatives to face-to-face (F2F) consultations in other departments, it is unclear whether this is feasible within the traditional Memory Clinic model. AIMS: The main aim of this service improvement project performed during the pandemic was to explore feasibility of telephone (TC) and videoconference (VC) Memory Clinic consultations. METHODS: Consecutive patients on the Memory Clinic waiting list were telephoned and offered an initial appointment by VC or TC. Data extracted included: age, internet-enabled device ownership, reason for and choice of Memory Clinic assessment. We noted Montreal Cognitive Assessment-Blind (TC) and Addenbrooke's Cognitive Examination-III (VC via Attend Anywhere) scores, and feasibility of consultation. RESULTS: Out of 100 patients, 12 had a home assessment, moved away, been hospitalised, or died. 45, 21 and 6 preferred F2F, VC and TC assessments respectively. 16 were not contactable and offered a F2F appointment. The main reason for preferring F2F was non-ownership, or inability to use an internet-enabled device (80%). VC and TC preference reasons were unwillingness to come to hospital (59%), and convenience (41%). Attendance rate was 100% for VC and TC, but 77% for F2F. Feasibility (successful consultations) was seen in 90% (VC) and 67% (TC) patients. CONCLUSION: For able and willing patients, remote Memory Consultations can be both feasible and beneficial. This has implications for future planning in dementia services.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".