Parkinson's disease and risk of brain tumor: a Meta-analysis
Bibliographic record
Abstract
<strong>Objective</strong> To assess the correlation between Parkinson's disease (PD) and brain tumor. <strong>Methods</strong> Taking Parkinson's disease, tumor, cancer in Chinese, and PD, Parkinson's disease, tumor, cancer, neoplasm in English as the key words, prospective cohort studies and case -control studies on relation between PD and brain tumor were searched by using PubMed, Web of Science, EMBASE/SCOPUS, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP database from January 1965 to July 2016, assisted by manual searching. Quality assessment and Mete-analysis were made by using Newcastle-Ottawa Scale (NOS) and Stata 12.1 software. <strong>Results</strong> A total of 11 studies with a total number of 350 632 PD patients were included in the overall analysis after excluding duplicate ones and those which did not meet the inclusion criteria from 1832 articles. Meta-analysis showed that compared with healthy people, PD patients had an increased risk of brain tumor (<em>OR</em> = 1.370, 95%CI: 1.120-1.690; <em>P</em> = 0.003), and the result was consistent after excluding 2 low-quality articles (<em>OR</em> = 1.360, 95% CI: 1.080-1.720; <em>P</em> = 0.008). Further stratified analyses according to disease onset time and regional difference showed that brain tumor occurrence in PD patients was significantly higher than healthy people only after the diagnosis of PD (<em>OR</em> = 1.430, 95% CI: 1.120-1.830; <em>P </em>= 0.004). The geographical subgroup analyses showed a higher risk of brain tumor among PD patients in Europe (<em>OR </em>= 1.420, 95%CI: 1.290-1.560; <em>P</em> = 0.000) and Taiwan area of China in Asia (<em>OR</em> = 2.590, 95% CI: 1.730-3.880; <em>P</em> = 0.000) compared with healthy people. Funnel plot, Begg test (<em>P</em> = 0.583) and Egger test (<em>P</em> = 0.985) showed there was no bias. <strong>Conclusions</strong> PD patients have a higher risk of brain tumor. <strong>DOI: </strong>10.3969/j.issn.1672-6731.2017.01.006
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".