Prognostic Value of Negative Emotions on the Incidence of Breast Cancer: A Systematic Review and Meta-Analysis of 129,621 Patients with Breast Cancer
Bibliographic record
Abstract
Breast cancer (BC) is one of the common malignant tumors in women and affects 1.6 million new cases globally each year. Investigators have recently found that negative emotions (NEs) and their impacts have greatly influenced the incidence and risk of BC. The present study aims to provide an association between NEs and the incidence of BC with possible risk factors. A total of 9343 studies were screened; nine studies met all inclusion criteria that were considered for the meta-analysis. The qualitative studies were measured by the Newcastle-Ottawa Scale; the observational studies were included with relative risks (RR) and corresponding 95% confidence intervals (CI). Besides the NEs and BC, the possible risk factors were evaluated. We analyzed data from 129,621 women diagnosed with NEs of which 2080 women were diagnosed with BC and their follow-up year ranges were from 4–24 years. NEs were significantly (p < 0.0001) associated with a higher incidence of BC with RR = 1.59, 95% CI:1.15–2.19, with other high-risk factors including, geographical distribution, emotion types, standard diagnosis of NEs, and follow-up duration. This study suggests that NEs significantly increase the risk for the incidence of BC, which can be supportive of the prognosis of the disease.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.020 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".