Inappropriate prescribing among older persons in primary care: protocol for systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
INTRODUCTION: Inappropriate prescribing has a significant impact on older persons in primary care. Previous reviews on inappropriate prescribing included a heterogeneous range of populations and may not be generalisable to primary care. In this study we aim to conduct a comprehensive systematic review and meta-analysis of the prevalence, risk factors and adverse outcome associated with inappropriate prescribing, specifically among older persons in primary care. METHODS AND ANALYSIS: We will search PubMed, Embase, CINAHL, Web of Science, Scopus, PsycINFO and references of other review articles for observational studies related to the keywords 'older persons', 'primary care' and 'inappropriate prescribing'. Two reviewers will independently select the eligible articles. For each included article, the two reviewers will independently extract the data and assess the risk of bias using the Newcastle-Ottawa Scale. If appropriate, meta-analyses will be performed to pool the data across all the studies. In the presence of heterogeneity, meta-regression and subgroup analyses will also be performed. The quality of the evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. ETHICS AND DISSEMINATION: The results will be disseminated through conference presentations and peer-reviewed publications. They will provide consolidated evidence to support informed actions by policymakers to address inappropriate prescribing in primary care, thus reducing preventable and iatrogenic risk to older persons in primary care. TRIAL REGISTRATION NUMBER: CRD42016048874.
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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 | 0.098 | 0.159 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.057 | 0.006 |
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".