The risk of debilitating falls (DF) in Manitobans living with cancer.
Bibliographic record
Abstract
e17596 Background: Falls and fall-related injuries pose important patient safety challenges. We sought to measure if cancer patients are at increased risk of DF. Methods: This retrospective population-based study was conducted by linking the Manitoba Cancer Registry with health care use records from Manitoba, Canada. All community-dwelling patients who were diagnosed with cancer from 2005-08 were matched with up to three cancer-free controls. DF were defined as falls requiring hospitalization and were identified using ICD-9 and -10 billing codes. A competing risk model was used to compare DF between cancer and cancer-free cohorts and expressed as sub-hazard ratios (SHR). Comparisons across groups were adjusted for age, sex, medication use, region of residence, and the presence of co-morbidities. Results: 22,327 cancer patients were matched to 67,927 controls. The median age was 66.5 years, and the median length of follow-up was 1.98 years. The cumulative 1 and 3-year incidence of DF are shown in the Table. (The adjusted risk of DF was significantly decreased in cancer patients versus matched controls ≥ 80 years old; SHR = 0.82 (95% CI: 0.57 – 0.97, p = 0.03). The adjusted SHR for stages I, II, III and IV in those ≥ 80 were 0.83 (95% CI: 0.64 - 1.09, p 0.18), 0.69 (95% CI: 0.51 - 0.93, p = 0.02), 0.56 (95% CI: 0.38 - 0.83, p < 0.01), and 0.45 (95% CI: 0.31 - 0.66, p < 0.01), respectively. Conclusions: The risk of DF in cancer and non-cancer patients increases with age. When compared to matched controls, only cancer patients ≥ 80 with advanced stage cancers were at decreased risk of DF. This finding is likely due to the higher competing risk of death in cancer patients (i.e., of those ≥ 80, 40.2% with cancer versus 4.0% without cancer die within one year). [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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".