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Record W2018347879 · doi:10.1177/1066896909332114

Intraoperative Consultation for Axillary Sentinel Lymph Node Biopsy: An 8-Year Audit

2009· article· en· W2018347879 on OpenAlexaff
Sharon Nofech‐Mozes, Wedad Hanna, Tulin Cil, May-Lynn Quan, Claire Holloway, Mahmoud A. Khalifa

Bibliographic record

VenueInternational Journal of Surgical Pathology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAxillaSentinel lymph nodeBiopsyAxillary Lymph Node DissectionBreast cancerMicrometastasisFrozen section procedureRadiologySurgeryGeneral surgeryCancerInternal medicine

Abstract

fetched live from OpenAlex

To summarize the authors' 8-year institutional experience with intraoperative consultation via frozen section (FS) on sentinel lymph node biopsy (SLNB) in breast cancer patients we recorded the, complete operative procedure including additional surgery on the ipsilateral axilla and intraoperative consultation and permanent histopathologic processing for all cases with inoperative consultation on SLNB in breast cancer patients between the groups, chi(2) and Fisher's exact tests were used. Intraoperative consultation was positive in 116/706 cases (16.4%) and final pathology in 158/706 cases (22.4%); the false-negative rate was 26.6%, the false-positive rate was 0%, and the overall accuracy was 94%. False-negative rate was significantly associated with the size of metastasis (micro vs macrometastasis; P < .002) but not significantly associated with the histologic type (P = 0.76) or pathologist expertise (P = 0.08). The rate of spared second procedures was 92% when calculated exclusively for patients who ultimately underwent ALND. Intraoperative consultation via FS for SLNB is a safe practice that can reliably save clinically node-negative patients a second surgery.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.381

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 head0.012
GPT teacher head0.301
Teacher spread0.290 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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