Efficacy comparison of breast conserving surgery versus radical mastectomy in triple negative breast cancer patients: a meta-analysis
Bibliographic record
Abstract
Objective To evaluate the effect of breast conserving surgery (BCS) versus radical mastectomy on the prognosis of triple negative breast cancer (TNBC) patients. Methods The databases (PubMed, Embase, MEDLINE, CNKI, VIP, WanFang) were searched for the related studies that met the requirements. Two reviewers independently screened the literature and extracted the data, such as number of cases, survival curve, hazard ratio (HR). The methodological quality of included studies was accessed using the Newcastle-Ottawa Scale (NOS). If HR was not mentioned in the papers, Engauge Digitizer 6.2 software was used to extract the survival curve data of each study, and then the ln(HR) and se[ln(HR)] of mastectomy versus BCS in the OS, DFS and local-regional recurrence-free survival (LRRFS) were calculated. If HR was mentioned in the papers, the ln(HR) and se[ln(HR)] were calculated directly. Finally, a meta-analysis was performed using RevMan 5.3 software. Results Totally 10 eligible studies were included, with 5 487 triple negative breast cancer patients involved. The NOS scores of all included studies were 7-9, indicating high methodological quality. In 10 cohort studies of TNBC, the OS of patients receiving BCS was significantly higher than that of patients receiving radical mastectomy (HR=1.25, 95%CI: 1.09-1.44, P=0.001). The DFS (HR=0.97, 95%CI: 0.72-1.30, P=0.830) and LRRFS (HR=1.11, 95%CI: 0.93-1.34, P=0.250) presented no significant difference between two groups. Conclusion In TNBC, BCS was superior to mastectomy in the OS, so BCS is recommended to the patients who meet the indications. Key words: Breast neoplasms; Surgery; Disease-free survival; Meta-analysis; Overall survival; Local-regional recurrence-free survival; Triple negative breast cancer
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.053 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".