Towards subgroup-specific risk estimates: A meta-analysis of longitudinal studies on olfactory dysfunction and risk of Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Interest has risen in identifying individuals at high risk of incident Parkinson's disease (PD) to facilitate inclusion in neuroprotective treatment trials. Current risk estimates of prodromal markers are based on aggregated data of an entire population, but this approach disregards differences in risk estimates by subgroups of a population. In this proof of concept, we determine subgroup-specific risk estimates of olfactory dysfunction for incident PD. METHODS: PubMed, EMBASE and Cochrane were searched for prospective studies investigating the association between olfactory dysfunction and incident PD. Random-effects meta-analysis, subgroup analyses and meta-regression were performed to investigate general and subgroup risk estimates. RESULTS: Individuals with odor identification dysfunction seemed to be at greater risk of incident PD compared to controls without olfactory dysfunction (OR = 4.18; 95%CI [2.47-7.07]). Risk estimates were higher in studies that included higher percentages of women (regression slope β = 0.053 increase in log odds ratio per 1% increase 1%, p = 0.0006), increased with mean study age (β = 0.21 per one year increase; p = 0.005) and in REM-sleep behavior disorder cohorts (β = 1.95; p = 0.03). Furthermore, the association between olfactory dysfunction and incident PD was most distinct in studies with shorter follow-up duration (ß = -0.56; p = 0.0047). CONCLUSION: The presence of olfactory dysfunction conveys a considerably elevated risk of incident PD, likely more in studies with a higher proportion of women, older individuals or short follow-up duration. Individual patient data are warranted to confirm these findings and to yield subgroup-specific risk estimates of other common markers to refine prodromal PD criteria.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".