Extensive intraductal component as a factor determining local recurrence of breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Breast-conserving surgery and mastectomy are standard surgical options for breast cancer. However, some patients experience a local recurrence after the operation. Many factors have been identified as a risk of local recurrence. Extensive intraductal component (EIC) was found as one of the major risks of the recurrence. Nevertheless, there were neither any systematic reviews nor controlled trials focused on EIC. This study aims to identify the impact of EIC on the local recurrence of breast cancer. Methods: We searched all relevant studies published between the inception to December 2020. All electronic data from PubMed and Scopus databases were extracted for evaluation of EIC as a factor of the recurrence. Local recurrence was a primary outcome between EIC-positive group and EIC-negative group. Margin status and adjuvant radiation were focused as a subgroup analysis. The Newcastle Ottawa Scale was applied for quality assessment of included studies and RevMan 5.3 program was used to estimate the effect of the results. The odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: A total of 32 studies, comprising 4,290 and 15,143 patients in the EIC-positive and EIC-negative groups respectively, were retrieved and met selection criteria. All included studies were at low to intermediate risk of bias. There was a statistically significant difference in local recurrence between EIC-positive patients and EIC-negative patients (OR =2.73; 95% CI: 2.42-3.07; P<0.00001). However, there was not any significant difference in patients who had negative margin (OR =1.97; 95% CI: 0.92-4.19; P=0.36) or received any adjuvant irradiation (OR =1.58; 95% CI: 0.55-4.54; P=0.24). Conclusions: EIC increases the risk of local recurrence, especially in breast-conserving surgery patients. However, there are a limited number of populations to analyze in subgroup analysis, the rate of local recurrence between two groups is not different in patients who had negative margin or received postoperative irradiation.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".