Quantification of anticholinergic and sedative drug load with the Drug Burden Index: a review of outcomes and methodological quality of studies
Bibliographic record
Abstract
PURPOSE: The Drug Burden Index (DBI) is a non-invasive method to quantify patients' anticholinergic and sedative drug burden from their prescriptions. This systematic review aimed to summarise the evidence on the associations between the DBI and clinical outcomes and methodological quality of studies. METHODS: A search in PubMed and Embase (search terms: 'drug', 'burden', and 'index') was performed and experts were contacted. We excluded publications that did not report empirical results or clinical outcomes. Methodological quality was assessed using the Newcastle-Ottawa Scale. Potential omissions of relevant clinical outcomes and populations were studied. RESULTS: Of the 2998 identified publications, 21 were eligible. Overall, methodological quality of studies was good. In all but one study, adjustment was made for prevalent co-morbidity. The DBI was examined in diverse older individuals, i.e. both males and females from different settings and countries. However, no studies were conducted in other relevant patient groups, e.g. psychiatric patients. Exposure to anticholinergic and sedative drugs was thoroughly ascertained, though the specific calculation of the DBI differed across studies. Outcomes were assessed from medical records, record linkage or validated objective tests or questionnaires. Many studies found associations between the DBI and outcomes including hospitalisation, physical and cognitive function. Cognitive function and quality of life were understudied and the number and scope of longitudinal studies was limited. CONCLUSIONS: An accumulating body of evidence supports the validity of the DBI. Longitudinal studies of cognitive function and quality of life and in other patient groups, e.g. psychiatric patients, are warranted.
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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 | 0.066 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".