Effect of hyperbaric oxygen therapy for non-motor symptoms among patients with Parkinson's disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective To systematically evaluate the efficacy of hyperbaric oxygen therapy for non-motor symptoms in patients with Parkinson's disease. Data sources A systematic search was performed across several databases, including the Chinese Science and Technology Periodical Database, Web of Science, SinoMed, PubMed, Cochrane Library, Embase and Wanfang databases up to 1 December 2024. Studies considered for inclusion comprised randomised controlled trials and pre–post control studies. Review methods The authors conducted the data extraction and literature screening procedures independently. The selected studies’ quality was evaluated using the Cochrane risk of bias technique. Review Manager 5.3 was used to conduct meta-analyses, while Stata 15.0 was utilised for sensitivity analysis and to assess publication bias. Results Sixteen studies with a total of 1324 individuals satisfied the inclusion criteria. The results of the meta-analysis showed that hyperbaric oxygen therapy significantly improved scores on the Hamilton Anxiety Scale and Hamilton Depression Scale compared with baseline (both p < 0.05). Additionally, improvements were observed in the Montreal Cognitive Assessment, Mini-Mental State Examination, Parkinson's Disease Sleep Scale, Pittsburgh Sleep Quality Index, Kubota's Drinking Water Test and Unified Parkinson's Disease Rating Scale III. Improvements in sleep efficiency and extension of total sleep time were also noted compared with controls (all p < 0.05). Conclusion The findings indicate that hyperbaric oxygen therapy effectively alleviates non-motor symptoms such as anxiety, depression, cognitive impairments, sleep disturbances, and swallowing dysfunction, as well as improving motor function in Parkinson's disease patients. Since the hyperbaric oxygen therapy protocol had significant differences between studies, further prospective randomised controlled trials with rigorous methodology are needed.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.033 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".