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Record W3048733132 · doi:10.14740/wjon1289

Sarcopenia and Visceral Adiposity Are Not Independent Prognostic Markers for Extensive Disease of Small-Cell Lung Cancer: A Single-Centered Retrospective Cohort Study

2020· article· en· W3048733132 on OpenAlexvenueno aff
Seigo Minami, Shôichi Ihara, Kiyoshi Komuta

Bibliographic record

VenueWorld Journal of Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSarcopeniaInternal medicineOncologyHazard ratioProportional hazards modelBody mass indexMultivariate analysisChemotherapyPerformance statusLung cancerRetrospective cohort studyConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Sarcopenia and visceral adiposity have been suggested to affect prognosis and treatment efficacy in various types of cancers. The aim of our study was to evaluate whether pretreatment sarcopenia and visceral adiposity are associated with prognosis in patients with extensive-disease small-cell lung cancer (ED-SCLC). METHODS: Between September 2007 and March 2018, 128 ED-SCLC patients received first-line and platinum-based chemotherapy at our hospital. Based on pretreatment body mass index (BMI), psoas muscle index (PMI), intramuscular adipose tissue content (IMAC) and visceral-to-subcutaneous fat ratio (VSR) at lumbar vertebra L3 level, we divided these patients into two groups, and then compared overall survival (OS) and progression-free survival (PFS). Adjusted by age, serum albumin, lactate dehydrogenase (LDH), clinical stage and performance status, we detected independent prognostic factors by multivariate Cox proportional hazard analyses. RESULTS: We did not find any significant differences in OS and PFS between two groups divided by BMI, PMI, IMAC and VSR. According to multivariate analyses, none of BMI, PMI, IMAC and VSR was an independent prognostic factor of OS and PFS. CONCLUSIONS: Neither pretreatment sarcopenia nor visceral adiposity is a prognostic marker of patients with ED-SCLC treated with standard regimen of platinum-based chemotherapy.

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 score0.015
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.346
Teacher spread0.294 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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