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Record W2169378164 · doi:10.1093/jnci/djh142

Influence of the New AJCC Breast Cancer Staging System on Sentinel Lymph Node Positivity and False-Negative Rates

2004· article· en· W2169378164 on OpenAlexaff
David R. McCready, Wei Sean Yong, A. Ng, Naomi Miller, Susan J. Done, Bruce Youngson

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineSentinel lymph nodeSentinel nodeBreast cancerLymphadenectomyCohortLymphRadiologyLymph nodeBiopsyCancerOncologySurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

The sixth and newest edition of the American Joint Committee on Cancer (AJCC) staging system for breast cancer now defines axillary sentinel lymph nodes with micrometastatic deposits 0.2 mm in diameter or smaller as node-negative. The aim of this study was to determine how this new classification scheme would affect axillary sentinel lymph node positivity, false-negative rate, and overall accuracy of an inception cohort of 205 breast cancer patients undergoing definitive surgery that included sentinel lymph node biopsy plus level I/II axillary lymphadenectomy. Based on the previous AJCC system for staging breast cancer, in which all sentinel lymph node metastases were considered positive, the rate of nodal positivity in this cohort was 47%, the overall accuracy was 99%, and the false-negative rate was 2.1%. According to the new classification system, the rate of nodal positivity in this cohort was 39.5% and the overall accuracy was 98%. The false-negative rate rose to 4.9% because two patients with micrometastatic deposits 0.2 mm or smaller, which are considered node-negative in the new system, had macroscopically positive disease in non-sentinel lymph nodes found in the completion lymphadenectomy.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.327

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 head0.014
GPT teacher head0.290
Teacher spread0.277 · 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 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

Citations69
Published2004
Admission routes1
Has abstractyes

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