An Evaluation of Longitudinal Measures of Anticholinergic Exposure for Application in Retrospective Administrative Data Analyses
Bibliographic record
Abstract
As continuous exposure to anticholinergics has been associated with adverse outcomes, accurately measuring exposure is important. However, no gold standard measure is available, and the performance of existing measures has not been compared. Our objective was to compare the properties of the Cumulative Anticholinergic Burden (CAB) measure against two existing measures of anticholinergic exposure and to assess their compatibility for use in observational studies based on claims data. The average daily dose, cumulative dose and CAB measures were evaluated on: the applicability for use with anticholinergic burden scales, the ability to consider duration and/or accumulation of exposure, and consideration of anticholinergic dose, potency, and residual effect. To calculate each measure empirically, Truven MarketScan claims data from 2012 to 2015 were analyzed. Cumulative anticholinergic exposure over 1-year post-enrollment was calculated for each measure using Anticholinergic Cognitive Burden scale scores. Median [interquartile range (IQR)] and ranges of measure scores, and Spearman’s correlation coefficients between measures, were estimated. Due to the differing methods of calculation, the absolute values of each score cannot be compared. The properties of the different measures varied, with only the CAB considering both dose and theoretical potency. The cohort included 99,742 individuals (mean age = 73.1 years; 54.9% female). Among individuals prescribed anticholinergics ( n = 55,969), 1-year median (IQR) scores based on average daily dose, cumulative dose and CAB measures were 0.9 (0.3–1.5), 16.9 (7.3–33.9) and 203 (68–500), respectively. Measures were highly inter-correlated ( r 2 = 0.74-0.83). Considering both potency and dose, the CAB may prove a more comprehensive measure of anticholinergic burden; however, additional research is necessary to demonstrate whether it has any association with relevant health-related outcomes. Astellas Pharma Global Development, Inc.
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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 | 0.131 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".