Contrast of Mastoscopic and Conventional Axillary Lymph Node Dissection of Patients With Breast Cancer: Meta-Analysis
Bibliographic record
Abstract
Mastoscopic axillary lymph node dissection (MALND) is a currently used and safe surgical treatment option for breast cancer. However, the extensive application of MALND is still debatable because of the use of conventional axillary lymph node dissection (CALND). Therefore, in the current study, we aimed to compare the efficacy and safety of MALND and CALND for obtaining evidence-based conclusions about the short-term and long-term outcomes of MALND for patients with breast cancer. PubMed, Web of Science, Cochrane Library, and CNKI were comprehensively searched for articles published between January 1998 and January 2019. Then Newcastle-Ottawa scale was used for quality assessment. The Review Manager software version 5.0 was utilized for generating forest maps and funnel plots. Twelve studies including 2157 patients were selected for the meta-analysis. There were no significant differences in the number of lymph node dissections, tumor recurrence rate, axillary drainage, postoperative hospitalization time, and tumor size between the MALND and CALND groups ( P > .05). In the MALND group, the surgery time was longer, while the incidence of intraoperative bleeding was lesser and the duration of drainage was shorter than those in the CALND group ( P < .01). The complications in the MALND group were also fewer than those in the CALND group ( P < .05). The results of the current study showed that MALND is reliable and feasible for breast cancer owing to the lesser incidence of intraoperative bleeding, shorter drainage duration, and lower incidence of complications compared to CALND.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.044 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.003 | 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".