How to Identify Patients with Cancer at Risk of Falling: A Review of the Evidence
Bibliographic record
Abstract
BACKGROUND: Clinical experience and a limited number of studies suggest that a cancer diagnosis confers a high risk of accidental falls. The negative sequelae of falls in older persons are well documented; risk factors for falls in this population have been extensively investigated and evidence for the efficacy of interventions to reduce falls is steadily emerging. It is not known whether the risk factors for falls and effective interventions for falls risk reduction in patients with cancer are different from those in older persons. METHODS: Electronic databases MEDLINE, Embase, and CINAHL were searched for studies of risk factors for falls or effective interventions for falls risk reduction in patients with cancer. Assessment of study quality was performed. Data analysis was descriptive. RESULTS: Seven studies designed to identify the risk factors for falls in patients with cancer and one study to determine the predictive validity of a screening tool for falls in patients with cancer were included. All had methodological shortcomings, precluding the generation of a new synthesis from this review, but highlighting important design and statistical issues. CONCLUSIONS: Further research is needed to identify patients at risk and inform the design of an interventional model to reduce falls risk. Investigators should be cognizant of the limitations of using cross-sectional study design to answer this research question, should employ validated tools to measure exposure variables, use reliable methods to ascertain the occurrence of falls and appropriate statistical models to adjust for confounding variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".