Effect of treatment gaps in elderly patients with dementia treated with cholinesterase inhibitors
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of treatment gaps on the risk of institutionalization or death among community-dwelling elderly patients treated with cholinesterase inhibitors (ChIs). METHODS: A survival analysis was conducted among a cohort of community-dwelling elderly patients (age 66+) newly treated with ChIs identified in the Quebec drug claims databases (Régie de l'Assurance Maladie du Québec [RAMQ]) between January 1, 2000, and December 31, 2007. Treatment nonpersistence during the year following ChI initiation was defined as treatment discontinuation or gaps of at least 6 weeks. To account for reverse causality, Cox proportional hazard modeling was conducted only among patients who did not discontinue treatment, in order to assess the association between treatment nonpersistence and institutionalization or death. RESULTS: Among the 24,394 elderly ChI users, 4,108 (16.8) experienced a treatment gap during the year following ChI treatment initiation while 596 (2.4%) discontinued their treatment within the first 3 months (early stoppers) and 4,038 (16.6%) after 3 months of treatment (late stoppers). Of all treated patients, 4,409 (18.1%) were institutionalized or died during follow-up. In patients who did not stop their treatment, the risk of institutionalization or death appeared lower in patients who experienced a treatment gap (hazard ratio 0.91; 95% confidence interval 0.86-0.96). CONCLUSIONS: Our results suggest that, contrary to what was previously reported in clinical trials, treatment gaps do not compromise the outcome of patients treated with ChIs in a real-life setting.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".