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Use of lymph node ratio improves staging and selection criteria for adjuvant therapy of gastric cancer

2006· article· en· W2966733378 on OpenAlexaff
Natalie G. Coburn, Carol J. Swallow, Alex Kiss, Calvin Law

Bibliographic record

VenueJournal of Clinical Oncology · 2006
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsPrincess Margaret Cancer CentreWomen's College HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineLymph nodeHazard ratioCancerInternal medicineProportional hazards modelSurveillance, Epidemiology, and End ResultsPopulationEpidemiologyOncologyStage (stratigraphy)Cancer stagingAdjuvant therapyConfidence intervalCancer registry

Abstract

fetched live from OpenAlex

4051 Background: Despite 1997 American Joint Commission on Cancer (AJCC) guidelines stipulating assessment of ≥15 lymph nodes (LN) for staging of gastric cancer, only one third of patients in the Surveillance, Epidemiology and End Results (SEER) database from 1998–2002 had ≥15 LN assessed (ASCO 2005 #4004), with resultant understaging and probable under-treatment. In series from Asia and Europe, Lymph Node Ratio (LNR), the ratio of positive to total LN assessed, has been shown to be more accurate for staging than number of positive LN. However, most of these excluded cases with <15 LN assessed. We examined the utility of LNR in a North American population. Methods: Using SEER data, we identified 9503 M0 resected gastric cancer cases from 1988–2002. LNR was categorized as 0%, 1–10%, 11–30%, 31–50% and >50%. For node negative cases (LNR = 0%, n = 3652), we stratified by number of LN assessed (A=1–4; B = 5–9; C = 10–14; D≥15). For each AJCC stage or LNR strata, the degree of understaging in patients with inadequate LN assessment was measured by survival difference on Kaplan-Meier curves. Cox proportional hazard ratio (HR) models determined the effect of stratifying node negative patients and the accuracy of LNR for prognostication. Results: 27% of patients had a LNR > 50%, a high proportion compared to Asian series. Fewer nodes assessed resulted in a higher likelihood of being node negative. In node negative cases, the HR of death increased for those with fewer LN assessed (vs. Group D, with 95% CI): A: HR=1.6 (1.5–1.8); B: HR = 1.3 (1.1–1.5); C: HR = 1.3 (1.1–1.5). Understaging was observed for patients with inadequate LN assessment when AJCC criteria were used (p < 0.0001); this effect significantly decreased by using LNR. LNR had superior prognostic accuracy in Cox models. Conclusions: This study examines LNR in the largest series of resected gastric cancer in the literature, and the only one in which the majority of cases were inadequately staged. LNR significantly decreases understaging and improves prognostic ability. Node negative patients, nearly one third of cases, should be risk stratified by number of LN assessed, and considered for adjuvant therapy on this basis. LNR should be used to stratify node positive patients in clinical trials, and to provide more accurate staging and prognostication. No significant financial relationships to disclose.

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)0.005
metaresearch head score (Gemma)0.020
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 score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.140
GPT teacher head0.465
Teacher spread0.325 · 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".

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Citations2
Published2006
Admission routes1
Has abstractyes

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