The frequency of falls in patients with advanced cancer followed in an outpatient palliative care center.
Bibliographic record
Abstract
225 Background: Falls are a concern in patients who are frail, have advance age or severe underlying medical condition as a significant cause of morbidity and reduced quality of life. It has been reported to occur in up to 50% of palliative care patients in different settings. Our aim was to determine the frequency of falls and identify its predictors and correlates in patients with advanced cancer. Methods: We reviewed 1,984 consecutive patients with advanced cancer seen in the outpatient supportive care center and determined the frequency of patient reported falls within the last month prior to the visit. Baseline patient characteristics, symptom severity scores in the Edmonton Symptom Assessment Scale (ESAS), medication use, functional status, and use of assistive devices were used to identify factors that are predictive of falls using backwards stepwise logistic regression. Results: 1,041 (52%) were female, 1,377 (69%) were non-Hispanic-white, 343 (17%) had peripheral neuropathy, 237 (12%) were on psychotropic medications, and 1,140 (58%) were on opioids. There were 211 (11%) patient reported falls. Presence of brain metastasis, use of assistive device, ECOG, ESAS depression, and weight prior to visit were significantly associated with patient reported falls in the multivariate model. Conclusions: One tenth of patients seen in the outpatient supportive care clinic reported falls. Our findings showing that falls to be associated with brain metastasis, assistive device, functional status, ESAS depression and weight are useful in stratifying high risk patients. They also show that certain medications deemed as risk factors in other populations were not associated with falls. [Table: see text]
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".