A Population‐Based Study of Cholinesterase Inhibitor Use for Dementia
Bibliographic record
Abstract
OBJECTIVES: To examine current utilization patterns of cholinesterase inhibitor (ChEI) therapy for dementia to determine treatment duration, use in long-term care, how often patients receive these drugs until death, and frequency of switching between the available ChEIs. DESIGN: A population-based healthcare administrative database study. SETTING: Patients aged 66 and older from the Canadian province of Ontario who received a new prescription for a ChEI between June 1, 2000, and December 31, 2002. Patients were followed until discontinuation of ChEI therapy, death, or end of the observation period (March 31, 2005). PARTICIPANTS: Twenty-eight thousand nine hundred and sixty-one patients, including 4,601 residing in long-term care, mean age 80, 63% female. MEASUREMENTS: Information on diagnosis, medical comorbidity, physician visits, and concomitant medication use was obtained. Estimates of duration of continuous use were determined. The percentage of patients who remained on the initial dose prescribed, the proportion who switched to a second ChEI, and the percentage who remained on ChEIs until death were calculated. RESULTS: Patients had on average more than 26 physician visits in the year before ChEI therapy, but only 28% had seen a dementia specialist. Concomitant use of potentially inappropriate medications (strongly anticholinergic medications and benzodiazepines) was noted in 37% of patients. The average length of treatment for all patients was 866 days. Many patients (43%) remained on the initial dose prescribed, 6% switched to another ChEI, and 19% died while on ChEI therapy. CONCLUSION: Elderly patients with dementia are treated for lengthy periods of time with ChEIs in the community and in long-term care facilities. Further research is required to determine whether these utilization patterns are appropriate. It is also unclear whether these results are generalizable to other populations without universal health coverage or drug formulary benefits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".