Risk of fall with device-based advanced treatments in Parkinson’s disease: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) and infusion therapies are effective treatments for the motor complications of Parkinson's disease (PD), but less established is their role in fall prevention. This systematic review and network meta-analysis (NMA) aimed to evaluate the risk of falls associated with advanced therapies in PD. METHODS: Following PRISMA-NMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Network Meta-analyses) guidelines, we searched PubMed, Medline, Embase and CINAHL up to 20 March 2024. Eligibility criteria based on PICOS (Population Intervention Control Outcome Study design) framework were used for DBS of the subthalamic nucleus (STN) or globus pallidus pars interna (GPi), or infusion therapies, compared with best medical treatment (BMT) or sham stimulation. Pairwise meta-analysis was conducted using RevMan V.5.4, and NMA using the netmeta package in R software. RESULTS: Fourteen studies were included. A higher number of falls were observed in the DBS group compared with BMT, although the difference was not significant. Sensitivity analysis excluding a heterogeneity-contributing study showed a significantly higher fall risk in the DBS group (Risk Ratio (RR)=2.74, 95% CI 1.60, 4.67, p=0.0002). Subgroup analyses indicated that levodopa-carbidopa intestinal gel tended towards increased fall risk, while continuous subcutaneous infusion of (fos)levodopa (CSCI) significantly decreased risk with high certainty of evidence. NMA showed CSCI as the most effective in reducing falls, while STN DBS was associated with the highest risk. CONCLUSIONS: DBS, especially targeting the STN, may increase fall risk compared with other advanced non-DBS procedures. While LCIG might not alter fall risk, preliminary evidence suggests that CSCI positively affects fall prevention. PROSPERO REGISTRATION NUMBER: CRD42023420637.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.048 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".