Methods for evaluating the benefit and harms of deprescribing in observational research using routinely collected data
Bibliographic record
Abstract
Deprescribing is defined as "the planned and supervised process of dose reduction or stopping of medication that might be causing harm, or no longer be of benefit". Barriers to deprescribing include healthcare professional fear and lack of guidance. These may stem from limited available evidence on benefits and harms of deprescribing medications commonly used among older persons. Advances in pharmacoepidemiology and causal inference methods to evaluate comparative effectiveness and safety of prescribing medications have yet to be considered for deprescribing medication. This paper discusses select methods and how they can be applied to deprescribing research, using case studies of benzodiazepines and low-dose acetylsalicylic acid (aspirin). Target trial emulation involves the explicit application of design principles from randomised controlled trials to observational studies. Several design aspects, including defining eligibility criteria and time zero, require additional considerations for deprescribing studies. The active comparator new user design also presents challenges, including selection of an appropriate comparator. This paper discusses these aspects, and others, in relation to deprescribing studies. Furthermore, methods proposed to control for confounding, in particular, the prior event rate ratio and propensity scores, are discussed. Introduction of billing codes or mechanisms for accurately determining when deprescribing has occurred would enhance the ability to conduct research using routinely collected data. Although the approaches discussed in this paper may strengthen observational studies of deprescribing, their use may be best suited to certain scenarios or research questions, where randomised controlled trials may be less feasible.
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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 | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.103 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".