Anticholinergic burden and fractures: a protocol for a methodological systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Medications with anticholinergic activity are used in the treatment of many diseases common in old age, including depression, psychosis, Parkinson's disease, allergies, pain and urinary incontinence. A high anticholinergic burden (ACB) is considered a major risk factor for fractures in older adults but recent studies reported inconsistent results. These inconsistencies may partly be due to differences in methodological aspects. However, no systematic review so far has addressed this association and considered study methods. Thus, we aim to conduct a systematic review and meta-analysis of observational studies addressing the association of ACB with fractures and to provide a methodological appraisal of the included studies. METHODS AND ANALYSIS: We will search MEDLINE, EMBASE, the Science Citation Index, CENTRAL and grey literature using a strategy that combines the terms anticholinergic and fractures. We will hand search reference lists of articles. Two reviewers will independently screen all identified abstracts for eligibility and evaluate the risk of bias of the included studies using the Newcastle-Ottawa Quality Assessment Scale and RTI item bank. Discrepancies will be resolved by consensus or consultation with a third researcher. We will conduct a meta-analysis, either for the overall population or for specific and more homogeneous subgroups, if the number of studies retrieved and their heterogeneity allows it. ETHICS AND DISSEMINATION: No ethics approval will be sought, as no original data will be collected for this review. Findings will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42018116737.
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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 | 0.129 | 0.167 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.072 | 0.009 |
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".