Management of dementia by family physicians in academic settings.
Bibliographic record
Abstract
OBJECTIVE: To determine what proportion of patients with dementia seen by family physicians are assessed and managed according to the recommendations of the Canadian Consensus Conference on Dementia (CCCD). DESIGN: Retrospective medical record review. SETTING: Outpatient services in university-affiliated family practice clinics in Calgary, Alta; Ottawa, Ont; and Toronto, Ont. PARTICIPANTS: One hundred sixty patients who were diagnosed with dementia between January 1, 2000, and June 1, 2004. MAIN OUTCOME MEASURES: Use of the Mini-Mental State Examination (MMSE); collateral history; physical examination maneuvers; initial laboratory tests; diagnostic imaging; caregiver identification, assessment, and referral; driving assessment; specialist referral patterns; and other recommendations of the CCCD. RESULTS: The average age of patients assessed was 83 years; most patients (66.3%) were female. More than half (54.1%) were diagnosed with Alzheimer disease or vascular dementia. More than 25% of patients were not given a specific diagnosis: 13.1% were labeled as "dementia," and 12.5% as "not yet diagnosed." For most patients (69.6%) a collateral history was obtained and a primary caregiver identified (79.4%). Few physicians, however, assessed caregiver stress (33.1%) or referred caregivers for support (12.5%). Most patients (80.6%) seen by their family physicians for cognitive changes underwent at least one MMSE. The average score on the first MMSE was 23.5 (of 30) points. Most physicians ordered appropriate "basic" blood tests as part of their assessment. Forty percent of patients had computed tomographic examinations within 3 months of reporting symptoms of cognitive difficulties to their family physicians. Of these, 25% met the criteria for computed tomographic scan as recommended by the guidelines. Only 36.5% were asked about driving status or safety concerns and had this inquiry documented. Of those, 15.5% were referred for driving evaluations and 12.5% were reported to the Ministry of Transportation. CONCLUSION: There is fair to good compliance with recommendations of the 1999 CCCD guidelines. There is, however, little assessment of caregiver coping and referral of caregivers for support. Similarly, there is little assessment of driver safety and referral for formal driving evaluations. Computed tomographic imaging as part of the evaluation of dementia is overused.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".