The risk of debilitating falls in Manitobans living with prostate cancer (pc).
Bibliographic record
Abstract
244 Background: Falls and fall-related injuries are important patient safety problems. Some studies suggest that pc patients have higher fall rates, however the severity of these falls is unknown. We sought to measure if pc patients are at increased risk of a debilitating fall requiring hospitalization. Methods: This is a retrospective population-based study utilizing the Manitoba Cancer Registry and Manitoba Health administrative databases. Our cohort consists of all community-dwelling patients living in Manitoba Canada who were diagnosed with pc between 2004 and 2008. These individuals were matched by age, sex, and time of diagnosis with up to three cancer-free controls. Debilitating falls were defined as falls/fractures requiring hospitalization and were identified using ICD-9 and -10 billing codes. A competing risk model was used to compare debilitating falls between the pc and cancer-free cohorts and expressed as sub-hazard ratios. Follow-up ended December 31, 2009. Results: 2,903 pc patients were identified along with 8,686 matched controls. The mean age was 69.3 and 68.8 respectively. The median follow-up was 3.05 years. Debilitating falls were identified in 109 patients (3.8%) with pc and 345 (4%) matched controls. The cumulative incidence of debilitating falls for those with pc vs cancer-free controls were: 1.08% vs. 1.13% at 1-year and 5.25% vs. 5.96% at five years of follow-up (SHR = 0.95, 95% CI = 0.77 – 1.18, p = 0.65). On univariate analysis, patients with stage IV pc were at higher risk of falls compared to matched controls. This difference was not significant on multivariate analysis though (SHR = 1.19, 95% CI = 0.74 – 1.89, p = 0.48). On multivariate analysis, patients with a Gleason score of ≤6 experienced a reduced risk of debilitating falls compared to matched controls (SHR = 0.44, 95% CI = 0.27 – 0.72, p = 0.001), whereas patients with other Gleason scores did not. The analysis was similar when patients with fractures were excluded. Conclusions: In this large population-based study, the 1- and 5-year cumulative incidence of debilitating falls did not differ significantly for patients with vs without pc. In fact, compared to matched controls, low grade pc patients were less likely to experience a debilitating fall.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".