P3‐388: Networking family practitioners group with memory clinics: Improving access to care for Alzheimer's disease and related dementia
Bibliographic record
Abstract
In 2007, CME evaluation in Sherbrooke area showed General practitioner (GP) need clinical diagnostic and treatment validation and support for evaluation more than lecture. In Mégantic, 90 minutes drive from Sherbrooke, our memory clinic offers monthly consultation since 15 years but work load is increasing. Starting spring of 2008, the implementation the “ Alzheimer disease program ” (ADP) in Mégantic with “ family medicine practice group” (FPG) include a) “Based on real cases”, 2 training sessions for GP, nurses, occupational therapist and social worker, b) a full day of training for the nurses of many FPG by an experienced nurse and a geriatrician c) development of a standardized evaluation adapted from our memory clinic d) ADP management algorithm. ADCP include: A) standardized evaluation including ADL, cognitive assessment, risks as driving, medication, nutrition, etc. B) Synthesis and investigation C) Follow-up evaluation and phone contact D) Referral flow sheet to different services including regional Alzheimer Society. The program was presented to the medical board and health workers of the CSSS (hospital, local social services). Since spring 2009, the program runs well and increase the satisfaction of families and health system. 1- Evaluation made first by nurse 2- Then GP make the diagnostic and begin the treatment. 3- Short term follow-up by nurse.4- Every month, cases evaluated by the ADP are discussed with the geriatrician (network consultation) 5-Some are selected for more “in deep” consultation by the geriatrician. 6- Conference and cases based sessions 2 times a year with all the interested health workers of the CSSS. The ADP already doubled the number of patients managed in all aspects of dementia. Treatment compliance increased and more patients are involved in all type of non- pharmaceutical and pharmaceutical research. Formal networking for management of dementia improve care and research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.148 | 0.020 |
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".