MétaCan
Menu
← Back to cohort
Record W4405048695 · doi:10.1182/blood-2024-201856

Assessment of Sarcopenia As a Measure of Frailty in Older Patients with Relapsed or Refractory Diffuse Large B-Cell Lymphoma

2024· article· en· W4405048695 on OpenAlexaffabout
Jodi Chiu, Michael MacNeill, Chai W. Phua, Andy Wong, Mujtaba Basharat, Rachel Kyle, Joy Mangel

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsParkwood InstituteToronto General HospitalUniversity Health NetworkWestern University
Fundersnot available
KeywordsSarcopeniaMedicineDiffuse large B-cell lymphomaInternal medicineAdverse effectLymphomaRetrospective cohort studyCohortCancerOncology

Abstract

fetched live from OpenAlex

Background: Diffuse large B cell lymphoma (DLBCL) is a curable, yet aggressive lymphoma that occurs predominantly in older adults. The challenge in treating older patients is accurately identifying frailty, which confers increased vulnerability to adverse treatment outcomes. Sarcopenia, the loss of muscle mass, is independently associated with an increased risk of all-cause mortality, and may correlate with frailty. We aimed to assess the prevalence of baseline sarcopenia in older patients with relapsed/refractory (R/R) DLBCL, and its correlation with clinical outcomes. Methods: We conducted a retrospective cohort study of patients aged >60 years with R/R DLBCL treated at a Canadian cancer centre over the past 5 years from 2018 to 2022. Baseline sarcopenia measurements via skeletal muscle index, standardized uptake value, and skeletal muscle density were measured at the level of L3 vertebra on PET-CT scans done at time of diagnosis of R/R disease, before treatment initiation. The presence or absence of sarcopenia was defined using methodology outlined by van Vugt et al (Eur J Clin Nutr 2019), where Z-score was generated using L3 muscle reference data on sex matched subjects age ≥60. Z-score < - 3 was defined as sarcopenic. Patient and lymphoma characteristics, adverse events, and clinical outcomes were also collected and analyzed. Results: 51 patients (34M/ 17F) with a median age of 72 (range 62 - 88) at R/R diagnosis were included. Forty-one (80%) patients were Stage III or IV, and 37 (73%) patients had a poor R-IPI score of 3 - 5. Patients received a median of 4 cycles of chemotherapy (range 1 - 8). Twenty-six (51%) patients received GDP-R, with 11 (22%) patients proceeding to autologous stem cell transplant. The prevalence of baseline sarcopenia was 49% (25/51), with a median Z-score of -2.88 (range -5.47 - -0.66). There was no significant association between sarcopenia and DLBCL stage, R-IPI, baseline ECOG score, or baseline BMI. There was no significant association between sarcopenia and chemotherapy regimen changes (i.e., dose decrease, drug or regimen discontinuation). There was no significant association between sarcopenia and occurrence of febrile neutropenia, infection requiring antibiotics, hospital or ICU admissions, bleeding complications, or transfusion requirements. There was also no significant difference in overall response rates (42% vs 62%), complete response rates (38% vs 57%), and overall survival (34% vs 36%) in the non-sarcopenic group compared to the sarcopenic group. Conclusion: There is a high prevalence of sarcopenia in patients with R/R DLBCL. Sarcopenia is becoming an increasingly recognized tool for risk stratification of frail patients to allow for individualization and optimization of various treatment intensities and options. This retrospective study serves as a historical control for our concurrent prospective study which incorporates both clinical frailty and sarcopenia assessments.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.319
Teacher spread0.298 · 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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueBlood→Same topicNutrition and Health in Aging→French-language works237,207→