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Record W1591142329 · doi:10.1002/jso.23720

Axillary reverse mapping in breast cancer: A Canadian experience

2014· article· en· W1591142329 on OpenAlexaffabout
Urve Kuusk, Nazgol Seyednejad, Elaine McKevitt, Carol Dingee, Sam M. Wiseman

Bibliographic record

VenueJournal of Surgical Oncology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsSt. Paul's HospitalSt. Joseph's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineLymphedemaSentinel lymph nodeAxillary Lymph Node DissectionBreast cancerSentinel nodeSurgeryBiopsyLymphatic systemLymph nodeRadiologyCancerInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to evaluate the axillary reverse lymphatic mapping (ARM) procedure for reducing the risk of arm lymphedema after breast cancer surgery. METHODS: The ARM procedure was carried out with a subareolar injection of technetium-99 sulfur colloid the morning of surgery, and a patent blue dye injection into the upper inner arm after anesthesia. RESULTS: Fifty-two women made up our study population. Thirty-seven patients underwent sentinel lymph node biopsy (SLNB) and 15 patients underwent an axillary lymph node dissection (ALND) for known nodal metastasis. The sentinel lymph node was identified in 36 of the 37 cases who underwent SLNB alone and in 12 of 15 patients who underwent on ALND. In 13 patients, both blue and radioactive lymph nodes or lymphatics were clearly identified (25%) and 5 patients had a clear crossover with nodes being both blue and hot. Only a single patient with crossover lymphatics had metastases present in their sentinel node. CONCLUSION: The ARM technique did not prevent identification of the SLN and we identified much greater crossover than reported. We had a single patient, who underwent a sentinel node biopsy, with mild arm lymphedema (1.9%) after 2 years of follow up.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.284
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations24
Published2014
Admission routes2
Has abstractyes

Explore more

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