Utilization of Health Services Before and After Diagnosis in a Specialist Rural and Remote Memory Clinic
Bibliographic record
Abstract
Background Limited research exists on the use of specific health services over an extended time among rural persons with dementia. The study objective was to examine health service use over a 10-year period, five years before until five years after diagnosis in the specialist Rural and Remote Memory Clinic (RRMC). Methods Clinical and administrative health data of RRMC patients were linked. Annual health service utilization of the cohort (N = 436) was analyzed for 416 patients pre-index (57.5% female, mean age 71.2 years) and 419 post-index (56.3% female, mean age 70.8 years). Approximately 40% of memory clinic diagnoses were Alzheimer’s disease (AD), 20% non-AD dementia, and 40% mild or subjective cognitive impairment or other condition. Post-index, 188 patients (44.9%) moved to permanent long-term care and were retained in the sample; 121 patients died (28.9%) and were removed yearly. Results Over the ten-year study period, a significant increase occurred in the average number of FP visits, all-type drug prescriptions, and dementia-specific drug prescriptions (all p <.001). The highest proportion of patients hospitalized was observed one year pre-index, the highest average number of specialist visits was observed one year post-index, and both demonstrated a significant decreasing trend in the five-year post-index period (p = .037). Conclusions A pattern of increasing FP visits and drug prescriptions over an extended period before and after diagnosis in a specialist rural and remote memory clinic highlights a need to support FPs in post-diagnostic management. Further research of longitudinal patterns in health service utilization is merited.
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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 | 0.000 | 0.002 |
| 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 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".