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Associations between sarcopenia and clinical outcomes in men with metastatic castrate-resistant prostate cancer.

2023· article· en· W4379281982 on OpenAlexaff
Efthymios Papadopoulos, Andy Kin On Wong, Sharon Hiu Ching Law, L Zhang, Henriette Breunis, Urban Emmenegger, Shabbir M.H. Alibhai

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsPrincess Margaret Cancer CentreSunnybrook Health Science CentreHealth Sciences CentreUniversity Health Network
Fundersnot available
KeywordsSarcopeniaMedicineProstate cancerInternal medicineGrip strengthObservational studyGeriatric oncologyCancerOncologySurgery

Abstract

fetched live from OpenAlex

12056 Background: Understanding the impact of sarcopenia on clinical outcomes in patients with metastatic cancer will assist clinicians with risk stratification, treatment decision-making, and inform the need for targeted supportive care strategies. Our objective was to comprehensively assess sarcopenia using measures of muscle mass (radiographically) and function (i.e., muscle strength and walking speed) and examine its impact on severe treatment toxicity, time to first emergency room (ER) visit, prostate-specific antigen (PSA) progression, radiographic progression, and overall mortality in men initiating androgen receptor-axis targeted therapy (ARAT) or chemotherapy for mCRPC. Methods: This was a secondary analysis of a prospective observational study of older men with mCRPC at the Princess Margaret Cancer Centre. Sarcopenia was defined as the combination of low muscle strength (grip strength < 35.5kg), slowness (walking speed < 0.8m/s), and low muscle quantity or quality prior to treatment initiation. The skeletal muscle index and skeletal muscle density were assessed through computed tomography scans prior to ARAT or chemotherapy initiation to determine muscle quantity and quality, respectively, using published cut-offs. Severe treatment toxicity, unplanned healthcare use, and disease progression were assessed from treatment initiation until treatment termination or loss to follow up. The associations between sarcopenia and severe treatment toxicity (i.e., grade 3+ toxicity) were assessed using multivariable logistic regression. Survival analyses were used to assess the impact of sarcopenia on the time to first ER visit, PSA progression, radiographic progression, and overall mortality. An interaction term for sarcopenia by treatment was introduced in all multivariable models and when necessary, an analysis by treatment was performed. Results: In total, 110 men participated, of whom 30 (27.3%) had sarcopenia prior to initiating treatment. Sarcopenia was associated with severe treatment toxicity (adjusted odds ratio (aOR) = 6.26, 95%CI = 1.17-33.58, P = 0.032) and time to first ER visit (adjusted hazard ratio (aHR) = 4.41, 95%CI = 1.26-15.43, p = 0.020) in the group that was initiating ARAT but not chemotherapy. Sarcopenia was a significant predictor of radiographic progression (aHR = 2.39, 95%CI = 1.06-5.36, p = 0.035) and overall mortality (aHR = 2.44, 95%CI = 1.17-5.08, p = 0.018) in the entire cohort. Conclusions: Sarcopenia may predict severe treatment toxicity and emergency room visits in men starting ARAT for mCRPC. Additionally, sarcopenia predicts radiographic progression and overall mortality regardless of treatment type in men with mCRPC. Confirmation is needed from large-scale studies. Assessment of sarcopenia can assist clinicians in treatment decision making while identifying high-risk patients that require targeted supportive care strategies.

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)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.304
GPT teacher head0.578
Teacher spread0.274 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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