Personality Factors in Colorectal Cancer: A Systematic Review
Bibliographic record
Abstract
Background: The role of personality in cancer incidence and development has been studied for a long time. As colorectal cancer (CRC) is one of the most prevalent cancer types and linked with lifestyle habits, it is important to better understand its psychological correlates, in order to design a more specific prevention and intervention plan. The aim of this systematic review is to analyze all the studies investigating the role of personality in CRC incidence. Methods: All studies on CRC and personality up to November 2020 were scrutinized according to the Cochrane Collaboration and the PRISMA statements. Selected studies were additionally evaluated for the Risk of Bias according to the Newcastle-Ottawa Scale (NOS). Results: Eight studies met the inclusion criteria and were eventually included in this review. Two main constructs have been identified as potential contributors of CRC incidence: emotional regulation (anger) and relational style (egoism). Conclusion: Strong conclusions regarding the influence of personality traits on the incidence of CRC are not possible, because of the small number and the heterogeneity of the selected studies. Further research is needed to understand the complexity of personality and its role in the incidence of CRC and the interaction with other valuable risk factors.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".