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Record W4401908679 · doi:10.1002/jso.27841

Breast cancer‐related lymphedema: A comprehensive analysis of risk factors

2024· article· en· W4401908679 on OpenAlexaff
Charalampos Siotos, Sydney Arnold, Michelle Seu, Lilia Lunt, Jennifer Ferraro, Daniel Najafali, George Damoulakis, Joshua Vorstenbosch, Babak J. Mehrara, Anuja K. Antony, Deana Shenaq, George Kokosis

Bibliographic record

VenueJournal of Surgical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsLymphedemaMedicineBreast cancerAxillary Lymph Node DissectionStage (stratigraphy)Sentinel lymph nodeInternal medicineOdds ratioLogistic regressionProportional hazards modelSurgeryCancerOncology

Abstract

fetched live from OpenAlex

BACKGROUND: Breast cancer-related lymphedema is a devastating condition that negatively affects the quality of life of breast cancer survivors. We sought to identify risk factors that predicted the timing and development of lymphedema. METHODS: Women with breast cancer that underwent sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND) at our institution between 2007 and 2022 were identified and sociodemographic and clinical information was extracted. We used logistic regression analysis to identify risk factors for lymphedema and performed cox-regression analysis to predict the timing of lymphedema presentation after surgery. RESULTS: We identified 1,223 patients, of which 161 (13.2%) developed lymphedema within 1.8 (mean, SD = 2.5) years postoperatively. Patients with SLNB had significantly lower odds for lymphedema development (vs. ALND, OR = 0.29 [0.14-0.57]). Patients between 40 and 49 years of age, and 50-59 (vs. <40 years, OR = 2.14 [1.00-4.60]; OR = 2.42, [1.13-5.16] respectively), African American patients (vs. Caucasian, OR = 1.86 [1.12-3.09]), patients with stage II, III, and IV disease (vs. stage 0, OR = 3.75 [1.36-10.33]; OR = 6.62 [2.14-20.51]; OR = 9.36 [2.94-29.81]), and patients with Medicaid (vs. private insurance, OR = 3.56 [1.73-7.28]) had higher rates of lymphedema. Cox-regression analysis showed that African American (HR = 1.71 [1.08-2.70]), higher BMI (HR = 1.03 [1.00-1.06]), higher stage (stage II, HR = 2.22 [1.05-7.09]; stage III, HR = 5.26 [1.86-14.88]; stage IV, HR = 6.13 [2.12-17.75]), and Medicaid patients (HR = 2.15 [1.12-3.80]) had higher hazards for lymphedema. Patients with SLNB had lower hazards for lymphedema (HR = 0.43 [0.87-2.11]). CONCLUSION: Lymphedema has identifiable risk factors that can reliably be used to predict the chances of lymphedema development and enable clinicians to educate patients better and formulate treatment plans accordingly. LEVEL OF EVIDENCE: III (Retrospective study).

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.341
Teacher spread0.318 · 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".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

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