Association between cancer and neurodegenerative disorders including dementias; a systematic review
Bibliographic record
Abstract
Abstract Background Several studies have examined associations between neurodegenerative disorders (ND) including dementia, and cancer. However, the associations and directions of these relationships vary by types of ND and cancer. In the current context of ageing populations and increased incidence of ND, dementia and cancer, and existing comorbidity between these disease groups, a better understanding of these relationships could, in future, inform prevention and therapeutics. This systematic review summarises the epidemiological evidence on these associations. Methods PubMed, MEDLINE, Embase, Scopus and Web of Science were searched to identify relevant studies published by 31/12/2018. The search strategy included a combination of search and MESH terms related to ND (e.g. Alzheimer’s, dementia), cancer and study design (case-control, cohort). The quality of included studies was assessed using the Newcastle-Ottawa scale (NOS). Results 77 studies were eligible for inclusion. The majority of studies scored 6+ on the NOS scale and some reported significant associations between ND and cancer. The association with specific types of cancer was not as evident as with all cancers. An inverse relationship was found between NDs and particularly Alzheimer’s, Parkinson’s, and Dementia and Cancer. Only one study found no association between Vascular Dementia and Cancer. Conclusions The findings report an overall inverse association between NDs and all cancers but associations are less evident with specific cancer types. Results from this review can be helpful in recommending reporting standards for future research to reduce heterogeneity between studies. Key messages Exploring the intersection of neurodegenerative disorders/dementia and cancer might help redirect research to novel therapeutic approaches. A standardised approach in design and outcome measurement is necessary to reduce heterogeneity across the studies.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".