Efficacy and Safety of Pedunculopontine Nuclei (PPN) Deep Brain Stimulation in the Treatment of Gait Disorders: A Meta-Analysis of Clinical Studies
Bibliographic record
Abstract
BACKGROUND: Pedunculopontine nucleus (PPN) has complex reciprocal connections with basal ganglia, especially with internal globus pallidus and substantia nigra, and it has been postulated that PPN stimulation may improve gait instability and freezing of gait. In this meta-analysis, we will assess the evidence for PPN deep brain stimulation in treatment of gait and motor abnormalities especially focusing on Parkinson disease patients. METHODS: PubMed and Scopus electronic databases were searched for related studies published before February 2014. Medline (1966-2014), Embase (1974-2010), CINAHL, Web of Science, Scopus bibliographic, and Google Scholar databases (1960-2014) were also searched for studies investigating effect of PPN deep brain stimulation in treatment of postural and postural instability and total of ten studies met the inclusion criteria for this analysis. RESULTS: Our findings showed a significant improvement in postural instability (p<0.001) and motor symptoms of Parkinson disease on and off medications (p<0.05), but failed to show improvement in freezing of gait. CONCLUSIONS: Despite significant improvement in postural instability observed in included studies, evidence from current literature is not sufficient to generalize these findings to the majority of patients.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".