The effect of multidisciplinary team on survival rates of women with breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Breast cancer is quite frequent all around the world. This disease was responsible for an estimated 2.1 million malignancies in 2022, making it the seventh-highest cause of cancer deaths globally. A multidisciplinary team (MDT) care policy was developed in the United Kingdom (UK) in 1995 to enhance the quality of care for cancer patients. The purpose of this systematic review and meta-analysis study is to assess the effects of MDT on breast cancer survival rates. Methods: This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Systematic search was conducted in several international databases including Google Scholar, PubMed, EBSCOhost, and Proquest from 2012 to 2022. The authors used RevMan 5.4 to do the meta-analysis of the pooled hazard ratio. Newcastle-Ottawa Scale to measure the risk of bias. Newcastle-Ottawa Scale evaluated participant selection, comparability, and reporting of results using eight subscale items. Egger's test funnel plot was used to assess the potential publication bias for this study. Results: A total of 1187 studies were identified from research database. The authors found a total of six studies from six different countries (China, the UK, Taiwan, Australia, Africa, and France) included for this study. Based on the meta-analysis of the pooled hazard ratio of the included studies, the authors found that the overall effect size of the study was 0.80 (CI 95%: 0.73-0.88). Conclusions: Breast cancer patients who participated in well-organized MDT discussions had a greater survival rate than those who did not.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.059 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".