The Impact of Frailty on Palliative Care Receipt, Emergency Room Visits and Hospital Deaths in Cancer Patients: A Registry-Based Study
Bibliographic record
Abstract
Background. Eastern Cooperative Oncology Group (ECOG) performance status is used in decision-making to identify fragile patients, despite the development of new and possibly more reliable measures. This study aimed to examine the impact of frailty on end-of-life healthcare utilization in deceased cancer patients. Method. Hospital Frailty Risk Scores (HFRS) were calculated based on 109 weighted International Classification of Diseases 10th revision (ICD-10) diagnoses, and HFRS was related to (a) receipt of specialized palliative care, (b) unplanned emergency room (ER) visits during the last month of life, and (c) acute hospital deaths. Results. A total of 20,431 deceased cancer patients in ordinary accommodations were studied (nursing home residents were excluded). Frailty, as defined by the HFRS, was more common in men than in women (42% vs. 38%, p < 0.001) and in people residing in less affluent residential areas (42% vs. 39%, p < 0.001). Patients with frailty were older (74.1 years vs. 70.4 years, p < 0.001). They received specialized palliative care (SPC) less often (76% vs. 81%, p < 0.001) but had more unplanned ER visits (50% vs. 35%, p < 0.001), and died more often in acute hospital settings (22% vs. 15%, p < 0.001). In multiple logistic regression models, the odds ratio (OR) was higher for frail people concerning ER visits (OR 1.81 (1.71–1.92), p < 0.001) and hospital deaths (OR 1.66 (1.51–1.81), p < 0.001), also in adjusted models, when controlled for age, sex, socioeconomic status at the area level, and for receipt of SPC. Conclusion. Frailty, as measured by the HFRS, significantly affects end-of-life cancer patients and should be considered in oncologic decision-making.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".