End-of-life care for Medicare beneficiaries with ovarian cancer: Evaluation of intensity and rate of hospitalizations.
Bibliographic record
Abstract
9523 Background: Patients with advanced cancer are receiving increasingly aggressive medical care at the end-of-life (EOL). Population-based studies have not examined the medical care that ovarian cancer patients receive near death. Methods: We identified a national cohort of 6,956 Medicare beneficiaries who were living in Surveillance, Epidemiology, and End Results (SEER) areas, were diagnosed with epithelial ovarian cancer between 1996 and 2007, and died from ovarian cancer by December 2007. Using multivariable models, we examined rates of aggressive medical care within 30 days of death over time and examined indications for hospitalizations near death. Results: Adjusted rates of intensive care unit (ICU) admissions and emergency department (ED) visits increased significantly between 1996 and 2007 (ICU: 6.4% to 16.6%, p<0.0001 and ≥2 ED visits: 19.7% to 32.1%, p<0.0001). In contrast, late (within 7 days death) or absent hospice referrals decreased (63.1% to 47.8%, p<0.001) and chemotherapy use within 30 days of death decreased slightly (8.1% vs. 7.1%; p=0.04). Although terminal hospitalizations decreased (28.0% to 19.1%, p=0.001), rates of hospitalizations near death increased over time (41.4% vs. 45.3%, p=0.01). The most common indications for hospitalization included: bowel obstructions (20.0%), infections (10.4%), fluid or electrolyte abnormalities (9.2%), and malignant effusions (8.1%). Conclusions: Despite significant increases in the use of hospice near death, utilization of ICUs, EDs, and acute inpatient care at the EOL rose significantly between 1997 and 2007 for older ovarian cancer patients. Future studies should examine whether this high-intensity health care is avoidable given evidence that high-intensity care is associated with lower patient quality-of-life near death and increased complications in bereaved caregivers.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".