Association between high-cost imaging and hospice use at the end of life of cancer patients.
Bibliographic record
Abstract
6504 Background: Use of high cost imaging modalities can inform prognosis and potentially de-escalate unnecessary care at the end of life of cancer patients, but it may also be associated with increased health care expenditures. This analysis investigated the association of hospital-level use of advanced imaging studies with aggressiveness of care near death, as measured by hospice enrollment. Methods: Using SEER-Medicare, patients who died from common solid tumors between 2002 and 2007 were identified and hospital-level utilization of CT, MRI and PET scans were categorized into quartiles. Patients were assigned to the hospital at which they spent the majority of their inpatient days during the last 6 months of life. Multivariate-adjusted logistic regression models based on tumor type were constructed to correlate imaging use with late as well as all hospice admissions, defined as enrollment within 3 days and 18 months of death, respectively. Results: A total of 7,876 breast, 31,933 lung, 12,877 colorectal and 9,137 prostate cancer patients were included. High cost imaging varied across tumor types with rates being highest for lung cancer (mean of 2.29 CT, 0.58 MRI and 0.21 PET scans per patient) and lowest for prostate cancer (mean of 1.25 CT, 0.41 MRI, and 0.01 PET scans per patient) in the 6 months before death. Overall hospice use was least frequent in colorectal cancer (50% of patients) while late hospice admission was most common in lung cancer (11% of patients). Hospitals within the top quartile of imaging use generally had decreased odds of hospice utilization and higher likelihood of late hospice enrollment across most cancer types (Table). Conclusions: In this large cohort, receipt of care near end of life in hospitals with the highest rates of advanced imaging use was associated with lower use of and later enrollment to hospice. High cost imaging is not correlated with de-escalation of aggressive care. [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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".