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Long-term oncologic outcomes after omitting axillary surgery in older women with early stage, node-negative breast cancer: A systematic review and meta-analysis.

2023· review· en· W4379285779 on OpenAlexaboutno aff
Mariam Rana, Soyon Lee, Alistair C Lindsay, Juan Godinez, Erik Vakil

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerMeta-analysisStage (stratigraphy)Sentinel lymph nodeRandomized controlled trialObservational studyAxillaInternal medicineOncologyCancer

Abstract

fetched live from OpenAlex

525 Background: In 2016, Choosing Wisely recommended the omission of routine sentinel lymph node biopsy (SLNB) in early stage, clinically node-negative, hormone receptor-positive, Her2-negative breast cancer in women ≥70 years old, although data supporting this was limited. Our study aimed to examine the long-term impact of omitting axillary staging in elderly women undergoing surgery for early stage, clinically node-negative breast cancer. Methods: A systematic review and meta-analysis was conducted. Medline (Ovid) and Embase were searched for published papers and abstracts using a systematic search strategy. Randomized and observational studies comparing women aged ≥70 years of age with early-stage, clinically node-negative breast cancer undergoing surgery for breast cancer with and without axillary staging, were included. Included studies reported at least one of the following outcomes: axillary recurrence (primary outcome), disease-free survival (DFS), breast cancer-specific survival (BCSS), and overall survival. Risk ratios (RR) were calculated as summary estimates for all outcomes. A weighted pooled mean difference and 95% CI was calculated using a random-effects inverse variance meta-analysis for each outcome. Heterogeneity was calculated using I2 statistics, and explored using meta-regression. The Newcastle-Ottawa Scale was used to assess the methodological quality of eligible trials, based on the selection of patients, comparability of cohorts, and the methods of outcome assessment. Results: Nine studies were eligible for meta-analysis, including data for 48,523 patients. For the primary outcome of axillary recurrence, data for 3,591 patients was meta-analyzed. Axillary staging was found to reduce the risk of axillary recurrence compared to no axillary staging, although this was not statistically significant (RR 0.59, 95% CI: 0.26 to 1.35, I2= 46.6%, p = 0.21). For overall mortality, data for 14,981 patients was meta-analyzed, and a statistically significant protective effect of axillary staging on overall mortality was demonstrated (RR 0.55, 95% CI: 0.33 to 0.90, I2= 78.1%, p= 0.003). No significant differences were observed in DFS (RR 1.02, 95% CI: 0.51 to 2.07, I2 = 0.0%, p = 0.37) and BCSS (RR 0.96, 95% CI: 0.57 to 1.62, I2 = 0.0%, p = 0.78). Conclusions: Omission of axillary surgery to stage the axilla may be associated with a higher risk of overall mortality in older women with early-stage breast cancer compared to those who undergo axillary surgery. Omission of axillary surgery in this patient population should be carefully tailored to the individual patient, taking into consideration co-morbidity, life expectancy, and formal measures of frailty. Randomized trials are required to further explore the oncologic safety of omitting axillary staging in women aged 70 years or older undergoing breast cancer surgery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.038
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.158
GPT teacher head0.469
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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