Falls among geriatric cancer patients: a systematic review and meta-analysis of prevalence and risk across cancer types
Bibliographic record
Abstract
BACKGROUND: Falls represent a significant health concern among the older adults, particularly geriatric cancer patients, due to their increased susceptibility from both age-related and cancer treatment-related factors. This systematic review and meta-analysis aimed to synthesize global data on the prevalence and risk of falls in this population to inform targeted fall prevention strategies. METHODS: Following PRISMA 2020 guidelines, we conducted a comprehensive search of PubMed, Embase, and Web of Science up to October 2024. Articles were screened using Nested Knowledge software by two independent reviewers. Eligible studies included those involving geriatric cancer patients aged 60 years or older reporting on fall prevalence. Quality assessment was performed using a modified Newcastle-Ottawa Scale, and meta-analysis was conducted using random-effects models with R software. RESULTS: = 100%). Country- and cancer-type-specific analyses revealed variability in fall prevalence, with breast cancer patients showing the highest prevalence. The comparative risk analysis did not show a statistically significant difference in fall risk between cancer patients and non-cancer controls. CONCLUSION: Falls are a prevalent and concerning issue among geriatric cancer patients, with substantial variability influenced by cancer type and study design. Personalized fall prevention strategies tailored to cancer-specific risk factors are essential. Further research is warranted to explore the complex interplay of cancer treatments, frailty, and fall risk in this vulnerable population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".