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Record W4410855932 · doi:10.2196/69936

Prevalence of Frailty and Its Predictors Among Patients With Cancer at the Chemotherapy Stage: Systematic Review

2025· review· en· W4410855932 on OpenAlexvenueaboutno aff
Zihe Song, Xian‐Liang Liu, Minhui Zhong, Chan Yu, Runa Zhang, Xia Duan

Bibliographic record

VenueJMIR Cancer · 2025
Typereview
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintStage (stratigraphy)MedicineCancerOncologyGerontologyInternal medicineComputer scienceBiology

Abstract

fetched live from OpenAlex

Background: Chemotherapy causes physiological, psychological, and social impairments in patients with cancer. Frailty reduces the effectiveness of chemotherapy and increases the toxicity associated with radiotherapy and chemotherapy, the possibility of chemotherapy failure, and adverse outcomes. However, factors affecting chemotherapy-related frailty in patients with cancer remain unclarified. Objective: This systematic review aimed to identify risk factors driving frailty progression during chemotherapy in patients with cancer. Methods: A comprehensive systematic search was conducted on PubMed, Web of Science, Embase, China National Knowledge Infrastructure, China Science and Technology Journal Database (VIP), and SinoMed for observational studies (cohort, cross-sectional, or case-control) on factors affecting the debility-of-chemotherapy stage in patients with cancers between the inception of the database and February 2025, with an updated search executed in May 2025. Literature screening, quality evaluation using the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality checklist, and data extraction were conducted independently by 2 authors. Meta-analysis, effect size combination, sensitivity analysis, and publication bias analysis were performed using RevMan (version 5.4; The Cochrane Collaboration) and R (version 4.4.3; R Foundation). Results: The analysis comprised 14 studies (8 cross-sectional, 2 repeated cross-sectional, 3 cohort, and 1 mixed-design), including 3879 patients with cancer and 23 influencing factors. Methodological quality assessment using Agency for Healthcare Research and Quality (mean 8.8, SD 1.3, 95% CI 7.9-9.7; SE 0.4) and Newcastle-Ottawa Scale (mean 8.0, SD 1.0, 95% CI 6.7-9.3; SE 0.6) revealed 73% (8/11) of cross-sectional studies as high-quality. The meta-analysis showed a 35% (95% CI 22%-50%) prevalence of frailty during chemotherapy in these patients. Cancer stage (odds ratio 1.99, 95% CI 1.64-2.42), chemotherapy frequency (odds ratio 2.60, 95% CI 1.83-3.70), transfer (odds ratio 2.18, 95% CI 1.50-3.17), hemoglobin (odds ratio 0.29, 95% CI 0.18-0.47), white blood cell (odds ratio 0.37, 95% CI 0.21-0.65), comorbidity (odds ratio 1.93, 95% CI 1.30-2.86), and hypoproteinemia (odds ratio 1.74, 95% CI 1.31-2.30) were risk factors for frailty in patients at the chemotherapy stage. Conclusions: Frailty during chemotherapy was strongly associated with advanced cancer stage, frequent treatment cycles, metastasis, anemia, leukopenia, comorbidities, and hypoproteinemia. Clinically actionable findings emphasized hemoglobin and albumin monitoring as preventive targets, while heterogeneity in assessment tools and population bias limited generalizability. The integration of frailty screening into chemotherapy workflows is urgent to mitigate treatment-related functional decline.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.347
Teacher spread0.323 · 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 designSystematic review
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

Citations10
Published2025
Admission routes2
Has abstractyes

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