Cancer and central nervous system disorders: protocol for an umbrella review of systematic reviews and updated meta-analyses of observational studies
Bibliographic record
Abstract
BACKGROUND: The objective of this study will be to synthesize the epidemiological evidence and evaluate the validity of the associations between central nervous system disorders and the risk of developing or dying from cancer. METHODS/DESIGN: We will perform an umbrella review of systematic reviews and conduct updated meta-analyses of observational studies (cohort and case-control) investigating the association between central nervous system disorders and the risk of developing or dying from any cancer or specific types of cancer. Searches involving PubMed/MEDLINE, EMBASE, SCOPUS and Web of Science will be used to identify systematic reviews and meta-analyses of observational studies. In addition, online databases will be checked for observational studies published outside the time frames of previous reviews. Eligible central nervous system disorders will be Alzheimer's disease, anorexia nervosa, amyotrophic lateral sclerosis, autism spectrum disorders, bipolar disorder, depression, Down's syndrome, epilepsy, Huntington's disease, multiple sclerosis, Parkinson's disease and schizophrenia. The primary outcomes will be cancer incidence and cancer mortality in association with a central nervous system disorder. Secondary outcome measures will be site-specific cancer incidence and mortality, respectively. Two reviewers will independently screen references identified by the literature search, as well as potentially relevant full-text articles. Data will be abstracted, and study quality/risk of bias will be appraised by two reviewers independently. Conflicts at all levels of screening and abstraction will be resolved through discussion. Random-effects meta-analyses of primary observational studies will be conducted where appropriate. Parameters for exploring statistical heterogeneity are pre-specified. The World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) criteria and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach will be used for determining the quality of evidence for cancer outcomes. DISCUSSION: Our study will establish the extent of the epidemiological evidence underlying the associations between central nervous system disorders and cancer and will provide a rigorous and updated synthesis of a range of important site-specific cancer outcomes. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016052762.
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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 | 0.162 | 0.221 |
| Meta-epidemiology (narrow) | 0.010 | 0.009 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.075 | 0.015 |
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".