Current Status and Research Trends in Deprescribing: A Bibliometric Review
Bibliographic record
Abstract
Background: Polypharmacy has emerged as a major global public health concern. To mitigate its adverse effects, deprescribing has been introduced and integrated into clinical practice. This study aims to analyze the current research landscape and identify emerging trends in deprescribing from a bibliometric perspective. Methods: Relevant studies on deprescribing published prior to December 2024 were retrieved from the Web of Science Core Collection database. Bibliometric analysis and visualization of co-authorship, citation, co-citation, co-occurrence, and burst detection were performed using VOSviewer, CiteSpace, and Bibliometrix. Results: A total of 1809 publications were identified, with a marked increase over the past decade. The field is dominated by contributions from developed countries, notably the United States, Australia, and Canada. Studies primarily focus on chronic conditions, such as psychiatric disorders, cardiometabolic diseases, and chronic pain, and the medications used to treat them. Influential publications highlighted barriers and facilitators of deprescribing, deprescribing tools, and deprescribing interventions and their associated outcomes. Burst detection analysis pointed to increasing attention on pharmaceutical care and implementation science. Conclusion: This study presents the first comprehensive bibliometric overview of deprescribing. The findings demonstrate that the field has grown rapidly but remains dominated by developed countries and a limited set of chronic diseases. The integration of implementation science frameworks emerges as a promising approach to enhance the design and evaluation of deprescribing interventions. Future studies should broaden their scope to include a wider range of diseases and medications, and encourage greater participation from developing countries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.035 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".