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Record W4404415673 · doi:10.1186/s12937-024-01037-w

Body composition as a prognostic factor in cholangiocarcinoma: a meta-analysis

2024· review· en· W4404415673 on OpenAlexaboutno aff
Lilong Zhang, Kunpeng Wang, Rongqiang Liu, Tianrui Kuang, Chen Chen, Yao Feng, Weixing Wang

Bibliographic record

VenueNutrition Journal · 2024
Typereview
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicineClinical nutritionInternal medicineMeta-analysisLipidologySurgical oncologyOncologyGastroenterologyClinical chemistry

Abstract

fetched live from OpenAlex

This investigation seeks to scrutinize the relationships between body composition metrics and the clinical outcomes observed in patients with cholangiocarcinoma (CCA). A comprehensive exploration was conducted across three prominent online databases: Embase, PubMed, and the Cochrane Library. This endeavor spanned the entirety of each database up to the cutoff date of September 29, 2023. To evaluate the quality of the included studies, the Newcastle-Ottawa scale was employed. This comprehensive analysis included a total of 26 articles with a combined patient cohort of 4398 individuals. The results demonstrated that CCA patients with low skeletal muscle index (SMI) had significantly inferior OS (HR: 1.93, p < 0.001) and RFS (HR: 2.02, p < 0.001), as well as a higher incidence of postoperative complications (OR: 1.69, 95% CI: 1.20-2.38, p < 0.001) compared to those with high SMI. The presence of sarcopenia in CCA patients was significantly related to poorer OS (HR: 1.96, p < 0.001) and RFS (HR: 2.05, p < 0.001), and a higher rate of postoperative complications (OR: 1.39, p = 0.049) in comparison to those without sarcopenia. Moreover, lower psoas muscle index (PMI) and myosteatosis were associated with shorter OS (PMI, HR: 1.56, p < 0.001; myosteatosis, HR: 1.49, p = 0.001) and RFS (PMI, HR: 2.16, p < 0.001; myosteatosis, HR: 1.35, p = 0.023). Our findings highlight incorporating body composition screening into clinical practice can help develop treatment strategies and optimize perioperative care, potentially improving patient outcomes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.326
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.142
GPT teacher head0.397
Teacher spread0.254 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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