The Role of Systematic Reviews and Meta-Analyses of Incidence and Prevalence Studies in Neuroepidemiology
Bibliographic record
Abstract
BACKGROUND: Systematic reviews and meta-analyses on the incidence and prevalence of neurological conditions are important methods of quantifying the burden and risk of disease. METHODS: The rigorous methodology required in order to minimize publication bias, account for study heterogeneity, and variation in study quality are described. When appropriate, a meta-analysis is a powerful statistical tool that can help synthesize a vast literature quantitatively, taking into account study heterogeneity. As the epidemiology of neurological conditions continue to be widely studied internationally, systematic reviews and meta-analyses have become essential. RESULTS: If not conducted carefully, systematic reviews and meta-analyses in neuroepidemiology may lead to erroneous conclusions. It is important to consider various methodological, clinical and statistical factors at all stages of the review and analysis process. Detailed documentation should be kept to assist in the reporting process. CONCLUSIONS: Published reporting standards should be consulted when conducting systematic reviews and meta-analyses of the incidence and prevalence of neurological conditions, though reporting standards specific to neuroepidemiology are urgently 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 | 0.562 | 0.780 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.029 | 0.033 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".