Risk factors for falls in hospitalized patients with cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
A primary cancer diagnosis has been confirmed as an important risk factor for falls, and the incidence of falls has been shown to be higher in patients who have undergone cancer treatment than in those who have not undergone cancer treatment. Falls during hospitalization increase the medical costs of additional treatment and falls-related mortality. Many falls are preventable and a good understanding of the predictors of falls in this population is needed. However, the risk factors for falls have not yet been identified. The purpose of this review was to identify the risk factors for falls in hospitalized patients with cancer. Eleven English and Chinese electronic databases were searched from their inception to April 2022 and the methodological quality of the included studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. Five studies involving 1237 patients with cancer were included. The meta-analysis identifies eleven risk factors for falls in hospitalized patients with cancer, including age, history of falls, opiates, benzodiazepines, steroids, antipsychotics, sedatives, radiation therapy, chemotherapy, the use of an assistive device and length of hospitalization. Based on the evidence presented in this article, healthcare workers have the capacity to help reduce fall risk through the development of preventive support strategies in this population. Multicenter, prospective studies of patients with cancer should be conducted to further identify and validate their risk factors for falls.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| 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".