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Record W2335211809 · doi:10.1097/pgp.0000000000000246

Nomogram to Predict Risk of Lymph Node Metastases in Patients With Endometrioid Endometrial Cancer

2015· article· en· W2335211809 on OpenAlexaff
Erqi L. Pollom, Christopher M. J. Conklin, Rie von Eyben, Ann K. Folkins, Elizabeth Kidd

Bibliographic record

VenueInternational Journal of Gynecological Pathology · 2015
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsSurrey Memorial Hospital
Fundersnot available
KeywordsNomogramEndometrial cancerMedicineLymph nodeOncologyPathologyCancerInternal medicine

Abstract

fetched live from OpenAlex

Pelvic lymphadenectomy in early-stage endometrial cancer is controversial, but the findings influence prognosis and treatment decisions. Noninvasive tools to identify women at high risk of lymph node metastasis can assist in determining the need for lymph node dissection and adjuvant treatment for patients who do not have a lymph node dissection performed initially. A retrospective review of surgical pathology was conducted for endometrioid endometrial adenocarcinoma at our institution. Univariate and multivariate logistic regression analysis of selected pathologic features were performed. A nomogram to predict for lymph node metastasis was constructed. From August 1996 to October 2013, 296 patients underwent total abdominal or laparoscopic hysterectomy, bilateral salpingo-oophorectomy, and selective lymphadenectomy for endometrioid endometrial adenocarcinoma. Median age at surgery was 62.7 yr (range, 24.9-93.6 yr). Median number of lymph nodes removed was 13 (range, 1-72). Of all patients, 38 (12.8%) had lymph node metastases. On univariate analysis, tumor size ≥4 cm, grade, lymphovascular space involvement, cervical stromal involvement, adnexal or serosal or parametrial involvement, positive pelvic washings, and deep (more than one half) myometrial invasion were all significantly associated with lymph node involvement. In a multivariate model, lymphovascular space involvement, deep myometrial invasion, and cervical stromal involvement remained significant predictors of nodal involvement, whereas tumor size of ≥4 cm was borderline significant. A lymph node predictive nomogram was constructed using these factors. Our nomogram can help estimate risk of nodal disease and aid in directing the need for additional surgery or adjuvant therapy in patients without lymph node surgery. Lymphovascular space involvement is the most important predictor for lymph node metastases, regardless of grade, and should be consistently assessed.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

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