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Record W4376505214 · doi:10.1080/01635581.2023.2212425

Sarcopenia in Lung Cancer: A Narrative Review

2023· review· en· W4376505214 on OpenAlexaff
Uzair Jogiat, Zaharadeen Jimoh, Simon R. Turner, Vickie E. Baracos, Dean T. Eurich, Eric L.R. Bédard

Bibliographic record

VenueNutrition and Cancer · 2023
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSarcopeniaMedicineLung cancerCachexiaCancerDiseaseIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

It has been over 10 years since the relationship between sarcopenia and lung cancer was first explored. Since then, sarcopenia research has progressed substantially, and the prognostic value of this condition is becoming increasingly apparent. Prior systematic reviews and meta-analyses have established sarcopenia to be negatively associated with disease-free and overall-survival, as well as a major risk factor for post-operative complications. The bulk of the literature has explored sarcopenia in the resectable setting, with less emphasis placed on studies evaluating this condition in advanced disease. In this up-to-date review, an examination of the literature exploring the association between sarcopenia and long-term outcomes in advanced lung cancer is provided. We further explore the association between adverse events of medical therapy and the role of sarcopenia as a predictor of tumor response. Finally, the interventions on sarcopenia and cancer cachexia are reviewed, with an emphasis placed on prospective studies.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.139
GPT teacher head0.508
Teacher spread0.370 · 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 designNot applicable
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

Citations16
Published2023
Admission routes1
Has abstractyes

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