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Associations between axillary staging, adjuvant treatment, and survival in older women with early-stage breast cancer: A population-based study.

2022· article· en· W4281859653 on OpenAlexaffabout
Matthew Castelo, Rinku Sutradhar, Neil Faught, Danilo Giffoni M. M. Mata, Ezra Hahn, Lena Nguyen, Lawrence Paszat, Danielle Rodin, Sabina Trebinjac, Eileen Rakovitch

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsHealth Sciences CentrePrincess Margaret Cancer CentreSunnybrook Health Science CentrePublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineBreast cancerProportional hazards modelStage (stratigraphy)Propensity score matchingCancer registryRelative survivalInternal medicinePopulationAdjuvant therapyObservational studyCohortSurvival analysisOncologyCancerConfounding

Abstract

fetched live from OpenAlex

577 Background: The Choosing Wisely guidelines recommend against surgical axillary staging (AS) in women ≥70 years with ER+/HER2- early stage breast cancer (BC). However, there has been little change in practice patterns, which may be influenced by observational studies reporting worse survival among women not receiving AS. Previous analyses did not take into account comorbidities, specific adjuvant treatments and HER2 status which may confound the association between AS omission and survival. This study examined the impact of AS omission on survival in older patients with Stage I/II BC, and secondarily emulated the Choosing Wisely population in a subgroup of those ≥70 years undergoing sentinel node biopsy (SLNB) vs. no AS for ER+/HER2- tumors. Methods: This was a population-based cohort study using linked health administrative data in Ontario, Canada. From the Ontario Cancer Registry, we identified women aged 65-95 years who underwent surgery for Stage I/II BC between 2010 and 2016. We excluded women who received neoadjuvant chemotherapy. To address confounding between those who did and did not receive AS, we built a propensity score model including patient and disease characteristics. Patients were weighted by propensity scores using overlap weights. Association with overall survival (OS) was calculated using weighted Cox proportional hazards models, and breast cancer-specific survival (BCSS) was calculated using weighted Fine and Gray models, adjusting for biomarkers and adjuvant treatments. Adjuvant treatment receipt was modelled with weighted log-binomial models. Results: Among 17,546 older women, 1,807 (10.3%) did not undergo AS, who were older, more comorbid, less likely to undergo mastectomy, and more likely to have tumors ≥ 2 cm. After propensity score weighting, baseline characteristics including comorbidity were balanced between the two groups. Women who did not undergo AS were less likely to receive adjuvant chemotherapy (adjusted RR 0.70. 95% CI 0.58-0.84), endocrine therapy (adjusted RR 0.85, 95% CI 0.82-0.89) and radiotherapy (adjusted RR 0.69, 95% CI 0.65-0.73). Unadjusted 5-year survival was lower for women who did not undergo AS (68.1%, 95% CI 65.8-70.2 vs. 87.6%, 95% CI 87.0-88.1; p< 0.001), and there was a higher 5-year incidence of BC deaths (7.6%, 95% CI 6.2-9.2 vs. 4.3%, 95% CI 3.9-4.7; p< 0.001). After weighting and adjustment, women who did not undergo AS continued to have worse OS (adjusted HR 1.13, 95% CI 1.03-1.24), however, there was no significant difference in BCSS (adjusted HR 1.00, 95% CI 0.78-1.26). The results among 6,286 ER+/HER2- women ≥70 years undergoing SLNB vs. no AS were similar for OS (adjusted HR 1.22, 95% CI 1.05-1.42) and BCSS (adjusted HR 1.08, 95% CI 0.67-1.76). Conclusions: The omission of AS in older women with early stage BC was associated with worse OS, reflecting selection bias, but no significant difference in BCSS.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.397
Teacher spread0.351 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2022
Admission routes2
Has abstractyes

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