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Record W7099084892

Diabetes and Risk of Parkinson’s Disease: An Updated Meta-Analysis of Case-Control Studies

2013· article· en· W7099084892 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicMedieval European History and Architecture
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetes mellitusMedical prescriptionDiseaseEpidemiologyPharmacoepidemiologyMEDLINESubgroup analysisRelative risk
DOInot available

Abstract

fetched live from OpenAlex

Background: Whether diabetes increases the risk of Parkinson’s disease (PD) is still inconclusive. The objective of this updated meta-analysis is to synthesize evidence from case-control studies that evaluated the association between diabetes and the risk of PD. Methods: Seven databases were searched to identify case-control studies that evaluated the association between diabetes and PD. The methodological quality of included studies was assessed using Newcastle-Ottawa scale. All data were analyzed using Review Manager 5.1 software. Subgroup analyses were also adopted, according to stratification on gender, geographic location, source of the control group, smoking, anti-diabetes drug prescription and duration of DM. Results: Fourteen studies fulfilled inclusion criteria for meta-analysis, yielding a total of 21395 PD patients and 84579 control subjects. Individuals with diabetes were found to have a negative association with future PD (OR 0.75; 95 % CI 0.58–0.98) in spite of significant heterogeneity. In subgroup analyses, the negative correlation was still found in studies from North America, non-PD control groups from general population, never smoking individuals, and DM ascertainment based on questionnaire or self-report. Stratification of gender and DM duration showed no significant association. No association was also found in European and Asian individuals, hospital-based controls, ever smoking subjects, DM assessment by medical record or physician diagnosis, and insulin prescription for DM.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.055
GPT teacher head0.244
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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