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Risk factors for breast cancer-related upper extremity lymphedema:a meta-analysis

2014· article· en· W3029374538 on OpenAlexaboutno aff
Yuhuan Xie, Qi Guo, Fenghua Liu, Ye Tian

Bibliographic record

VenueZhonghua fangshe zhongliuxue zazhi · 2014
Typearticle
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLymphedemaBreast cancerOdds ratioMeta-analysisBody mass indexRadiation therapyCochrane LibraryAxillary Lymph Node DissectionConfidence intervalInternal medicineRisk factorLymph nodeRelative riskOncologyCancerSurgerySentinel lymph node

Abstract

fetched live from OpenAlex

Objective To systematically evaluate the risk factors for upper extremity lymphedema after breast cancer treatment and the strength of their associations.Methods PubMed,Ovid,EMbase,and the Cochrane Library were searched to identify clinical trials published up to December 2012.The quality of included studies was assessed by the Newcastle-Ottawa Scale;data analysis was performed by Stata 10.0 and RevMan 5.2;the strength of associations between risk factors and breast cancer-related upper extremity lymphedema was described as odds ratio (OR) and 95% confidence intervals (CI).Results Twenty-two studies involving 10106 patients were included in the meta-analysis.The risk factors for upper extremity lymphedema after breast cancer treatment mainly included axillary lymph node dissection (OR =2.72,95% CI=1.06-6.99,P=0.038),hypertension (OR=1.84,95% CI=1.38-2.44,P=0.000),body mass index (OR =1.68,95% CI=1.22-2.32,P =0.001),and radiotherapy (OR =1.65,95% CI =1.20-2.25,P =0.002),while no significant associations were found for such factors as chemotherapy,age,number of positive lymph nodes,and number of dissected lymph nodes.Conclusions The incidence of upper extremity lymphedema is high among patients with breast cancer after treatment,and axillary lymph node dissection,hypertension,body mass index,and radiotherapy are the main risk factors for lymphedema after breast cancer treatment. Key words: Breast neoplasms;  Upper extremity lymphedema;  Risk factors

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
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)0.0040.000

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 head0.032
GPT teacher head0.291
Teacher spread0.258 · 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 teacher head, not a consensus.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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