Diagnosis and management of dementia in primary care: exploratory study.
Bibliographic record
Abstract
OBJECTIVE: To assess the current identification and management of patients with dementia in a primary care setting; to determine the accuracy of identification of dementia by primary care physicians; to examine reasons (triggers) for referral of patients with suspected dementia to the geriatric assessment team (GAT) from the primary care setting; and to compare indices of identification and management of dementia between the GAT and primary care network (PCN) physicians and between the GAT and community care (CC). DESIGN: Retrospective chart review and comparisons, based on quality indicators of dementia care as specified in the Third Canadian Consensus Conference on the Diagnosis and Treatment of Dementia, were conducted from matching charts obtained from 3 groups of health care providers. SETTING: Semirural region in the province of Alberta involving a PCN, CC, and a GAT. PARTICIPANTS: One hundred patients who had been assessed by the GAT randomly selected from among those diagnosed with dementia or mild cognitive impairment by the GAT. MAIN OUTCOME MEASURES: Diagnosis of dementia and indications of high-quality dementia care listed in PCN, CC, and GAT charts. RESULTS: Only 59% of the patients diagnosed with dementia by the GAT had a documented diagnosis of dementia in their PCN charts. None of the 12 patients diagnosed with mild cognitive impairment by the GAT had been diagnosed by the PCN. Memory decline was the most common reason for referral to the GAT. There were statistically significant differences between the PCN and the GAT on all quality indicators of dementia, with underuse of diagnostic and functional assessment tools and lack of attention to wandering, driving, medicolegal, and caregiver issues, and underuse of community supports in the PCN. There was higher congruence between CC and the GAT on assessment and care indices. CONCLUSION: Dementia care remains a challenge in primary care. Within our primary care setting, there are opportunities for synergistic collaboration among the health care professionals from the PCN, CC, and the GAT. Currently they exist as individual entities in the system. An integrated model of care is required in order to build capacity to meet the needs of an aging population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".