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Breast cancer after reduction mammoplasty: A population-based analysis of incidence, treatment, and screening patterns.

2022· article· en· W4286298155 on OpenAlexaffabout
Ashley Drohan, May Lynn Quan, Dale C. Birdsell, Shiying Kong, Yuan Xu

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsAlberta Health ServicesUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsReduction MammoplastyMedicineMammoplastyAugmentation MammoplastyBreast cancerPopulationBreast reductionMammographySurgeryIncidence (geometry)CancerMammaplastyInternal medicineBreast augmentation

Abstract

fetched live from OpenAlex

e18785 Background: The incidence of breast cancer after reduction mammoplasty has been demonstrated to be lower than the general population in several large registry trials performed in primarily European populations; North American data is lacking. Tissue rearrangement during reduction mammoplasty may lead to abnormal breast imaging results postoperatively resulting in more challenging screening.The purpose of this study was to describe the incidence and treatment of breast cancer after reduction mammoplasty in a more contemporary Canadian population, and to better understand the use of breast cancer screening modalities in these patients. Methods: This population-based retrospective analysis utilized the Discharge Abstract Database held by the Canadian Institute for Health Information (CIHI) and the National Ambulatory Care Reporting System (NACRS) to identify all women 20 or older who underwent reduction mammoplasty in Alberta, Canada between 2003 and 2007. The incidence and treatment of breast cancer was compared among patients who underwent reduction mammoplasty and age-sex matched controls in Alberta. Imaging utilization post mammoplasty, including use of mammography, ultrasound and breast biopsy was also compared between these two groups. Results: A total of 8,021 patients over 20 years old underwent reduction mammoplasty during the study period. Patients were followed for an average of 12.6 years. Most women (6,417, 80%) underwent reduction mammoplasty surgery between the ages of 20-50. Compared to controls, women who underwent reduction mammoplasty had more comorbidities (Charlson Comorbidities > 1: 5.2% vs 4.2% controls, p < 0.0001). Overall, 89 (1.1%) patients who underwent reduction mammoplasty developed breast cancer after surgery, compared to 453 (1.9%) controls (p < 0.0001). Among patients diagnosed with breast cancer, there was no difference in patient characteristics, tumor size, histology and grade between the two groups. Fewer patients presented with metastatic disease after reduction mammoplasty (0% vs 5.1%, p = 0.043). The surgical treatment differed between groups; patients who underwent reduction mammoplasty were significantly more likely to undergo mastectomy for breast cancer (41.6% vs 1.5%, p < 0.0001). Women who underwent reduction were more likely to undergo mammography (66.7% vs 58.7%, p < 0.001), ultrasound (29.2% vs 26.2%, p < 0.0001) and biopsy for benign disease (7.2% vs 6%, p = 0.0001) compared to controls. Conclusions: Despite an increased frequency of breast cancer screening, the incidence of breast cancer is lower after reduction mammoplasty compared to women who did not undergo breast reduction. After a diagnosis of breast cancer, surgical treatment patterns differ between groups despite similarities in tumor characteristics, whereby mastectomy is more common in those who have undergone breast reduction.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.424
Threshold uncertainty score0.842

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.418
Teacher spread0.355 · 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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Citations0
Published2022
Admission routes2
Has abstractyes

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