Telemedicine in a Rural Memory Disorder Clinic—Remote Management of Patients with Dementia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There are many reasons to develop telemedicine clinics for assessment and management of dementia. Time constraints, location, and poor weather conditions can all impact on the ability of patients and providers to attend rural clinics. The utility of telemedicine in the diagnosis of dementia and subsequent follow-up appears promising in the literature, as it provides a viable means of assessing cognition in patients in remote areas with limited access to medical specialists. METHODS #ENTITYSTARTX00026; RESULTS: This study explored the feasibility of introducing a telemedicine memory disorder follow-up clinic in a rural community. The evaluation of 32 clinic sessions found high levels of satisfaction, with over 90% of physicians and patients indicating that they'd be willing to use video conferencing again. Physicians overwhelmingly felt the sessions provided enough information to assist in clinical decision-making (96%), and patients and CCAC Case Managers/Geriatric Assessors felt able to present the same information by video conferencing as in person (92% for both groups). The telemedicine clinic provided a number of favourable results such as: timely access to specialist care in the patient's own community; fewer cancelled clinics; enhanced care transitions between the follow-up clinic and primary care with the support of a case manager/geriatric assessor; and enhanced follow-up for a complex patient population. In addition, the telemedicine initiative freed up spaces for "in-person" clinics. This allowed them to focus on new patient assessments. CONCLUSIONS: The high satisfaction rates amongst all key stakeholders affirm that telemedicine is a viable option and worth continued efforts at shaping and developing, particularly in regions where local physician specialists are a scare resource.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it