High-Cost Hospitalizations Among Elderly Patients With Cancer
Bibliographic record
Abstract
PURPOSE: Health care costs are driven by a small proportion of patients, and it is important to identify their characteristics to effectively manage their health care needs. We examined characteristics associated with high-cost inpatient visits of elderly patients with cancer using a national sample. METHODS: We identified 574,367 inpatient visits of individuals age 65 years or older with a cancer diagnosis using the 2014 National Inpatient Sample data, an all-payer sample of inpatient stays in the United States. High-cost visits were defined as those with a total cost at or above the 90th percentile. The remaining visits were defined as the lower-cost group. We examined patients' clinical characteristics and hospital characteristics for both groups. Logistic regression was used to identify characteristics associated with being in the high-cost group. RESULTS: The median visit cost in the high-cost group was $38,194 (interquartile range, $31,405 to $51,802), which was nearly five times the cost of the lower-cost group (median, $8,257; interquartile range, $5,032 to $13,335). Hematologic malignancies were the most common cancer in the high-cost group. Those in the high-cost group were more likely to have metastatic cancer. Compared with patients with no comorbidities, those with five or more comorbidities were four times more likely to be in the high-cost group (odds ratio, 4.08; 95% CI, 3.74 to 4.46). Patients with a greater number of procedures were also more likely to be in the high-cost group (odds ratio, 1.57; 95% CI, 1.52 to 1.61). CONCLUSION: High-cost cancer visits were five times more expensive than the remaining visits. Identification of high-cost visits and the associated factors may help provide tailored strategies to effectively manage costly inpatient admissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".