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Association of sarcopenia and treatment tolerability in older adults with advanced cancer: Secondary analysis of a nationwide NCI Community Oncology Research Program (NCORP) randomized clinical trial.

2023· article· en· W4379280693 on OpenAlexaff
Richard F. Dunne, Eva Culakova, Eric Roeland, Grant R. Williams, J. Diaz Arguello, Kah Poh Loh, Rachael Tylock, Mirza Faisal Beg, Karteek Popuri, Marcus D. Goncalves, Judith O. Hopkins, Jeffrey L. Berenberg, Vincent Vinciguerra, Po‐Ju Lin, Karen M. Mustian, Supriya G. Mohile

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMemorial University of NewfoundlandSimon Fraser University
FundersNational Institutes of Health
KeywordsMedicineSarcopeniaCommon Terminology Criteria for Adverse EventsTolerabilityInternal medicineGeriatric oncologyRandomized controlled trialAdverse effectCancerPerformance status

Abstract

fetched live from OpenAlex

12038 Background: Sarcopenia (sarc) is defined by reduced muscle function paired with low muscle mass or quality. Sarc is common in older adults, but the relationship between sarc and treatment tolerability in older patients (pts) with cancer has not been established. This secondary analysis of our nationwide University of Rochester NCORP Research Base randomized trial examines whether pre-treatment sarc was associated with treatment toxicity and survival in older pts with advanced cancers. Methods: Pts aged 70 and older with incurable solid tumors and lymphoma initiating a new high-risk systemic treatment were enrolled in GAP70+ (NCT02054741). GAP70+ was a cluster randomized trial evaluating whether a geriatric assessment intervention can reduce cancer treatment toxicity. This secondary analysis included 161 pts with baseline computed tomography (CT) scans and physical function testing. Sarc was defined as low muscle strength (5-time Chair Stand Test > 16.7 seconds) with reduced muscle mass (BMI and sex-specific cutoffs, cm/m2) or low muscle quality (Hounsfield units [HU] average radiodensity thresholds) utilizing validated cross-sectional CT imaging metrics. Sarc was defined as severe in presence of reduced physical performance (Timed-Up-And-Go > 13.5 seconds). We evaluated if the proportion of pts experiencing any grade 3-5 toxicity in the first 3 months of treatment using Common Terminology Criteria for Adverse Events (CTCAE) V4.0 was higher in those with sarc than those without sarc. Multivariate logistic regression was used to further explore this relationship. Multivariate Cox regression analysis was performed to evaluate the association of sarc and severe sarc with 1-year survival. Results: Mean age of the 161 participants included in this cohort was 76.6 years and 60.9% of were male; over half had gastrointestinal (34.8%) or lung cancer (23.6%). Sarc was found in 91 of 161 (56.5%) pts; 54 had severe sarc. Pts with sarc did not experience significantly more grade 3-5 toxicity compared to those without sarc (79.1% vs. 71.4%, p = 0.26). However, pts with sarc experienced more grade 3-5 non-hematologic toxicity (63.7% vs. 44.3%, p = 0.01) on univariate and multivariate analysis (OR 2.2, CI 1.1-4.5, p = 0.02). The association between sarc and worse survival at 1-year was not statistically significant (OR 1.5, CI 0.9-2.4, p = 0.13). Those with severe sarc did demonstrate significantly worse 1-year survival on multivariate analysis (HR 1.8, CI 1.1-2.9, p = 0.02). Conclusions: Older pts with cancer and sarc were more likely to suffer serious non-hematologic toxicity from cancer treatment. Furthermore, those with severe sarc had significantly worse survival. Evaluation for sarc before initiating treatment in older adults could help oncologists identify pts at higher risk of toxicity and poor survival. Clinical trial information: NCT02054741 .

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.308
GPT teacher head0.624
Teacher spread0.316 · 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 designRandomized trial
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

Citations1
Published2023
Admission routes1
Has abstractyes

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