The risks of debilitating falls (DFs) in patients (pts) with cancer: The Manitoba experience.
Bibliographic record
Abstract
6588 Background: Falls and fall-related injuries are significant pt safety challenges. We sought to identify if cancer pts were at greater risk of DFs. Methods: Using a retrospective population-based study design, we linked the Manitoba Cancer Registry with health care use records from Manitoba, Canada. Our study cohort consists of all adult community-dwelling pts with a first cancer diagnosis between April 1, 2003 and March 31, 2008, matched by age and gender to three cancer-free controls. DFs were defined as falls requiring hospitalization (ICD billing codes) between the time of cancer diagnosis and Dec 31, 2009. Regression models using death as a competing risk were used to compare DFs separately for those <65, 65-79 and 80+. Each model was adjusted for individual year of age, sex, medication use, neighborhood income, previous falls and co-morbidities. Results were expressed using sub-hazard ratios (SHR). Results: 27,164 cancer pts were matched to 83,928 controls; 50% of the overall cohort was female, with a median age of 68 years. For each group, the median length of follow-up ranged from 1.6 and 3.7 years and decreased with age. DFs occurred in 866 cancer pts (3.2%) vs. 2883 (3.4%) controls. Without adjustment, short-term (<1 year) DF risk was greater in cancer versus control pts (Table 1). For all pts except those 80+ this increased risk was explained by study covariates. Adjusted long-term (>1 year) DF rates were statistically lower in cancer pts 65+ years old vs. controls. The SHRs for death in cancer pts compared to controls during <1 and >1 years were: <65, 104.4 and 20.6 (p<.001); 65-79, 41.2 and 7.7 (p<.001); 80+, 26.6 and 3.7 (p<.001). Conclusions: In this population-based study, cancer pts were at increased risk of DFs compared to matched controls during the 1st year after diagnosis. The risk disappeared after adjusting for confounding factors except in those > 80. After 1 year of follow-up, cancer pts no longer had a higher risk of falls in part due to their higher risk of death. Subhazard ratios for cancer versus cancer-free cohorts. Age < 1 year of follow-up >1 year of follow-up Unadjusted Adjusted Unadjusted Adjusted <65 2.005 t 0.943 1.137 0.892 65–79 1.547 t 0.996 0.869 0.652 t 80+ 1.774 t 1.440 * 0.520 t 0.466 t t < 0.001;* < 0.05.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".