Evaluating claims-based indicators of the intensity of end-of-life cancer care
Bibliographic record
Abstract
OBJECTIVE: To evaluate measures that could use existing administrative data to assess the intensity of end-of-life cancer care. METHODS: Benchmarking standards and statistical variation were evaluated using Medicare claims of 48,906 patients who died from cancer from 1991 through 1996 in 11 regions of the United States. We assessed accuracy by comparing administrative data to 150 medical records in one hospital and affiliated cancer treatment center. RESULTS: Systems not providing overly aggressive care near the end of life would be ones in which less than 10% of patients receive chemotherapy in the last 14 days of life, less than 2% start a new chemotherapy regimen in the last 30 days of life, less than 4% have multiple hospitalizations or emergency room visits or are admitted to the intensive care unit (ICU) in the last month of life, and less than 17% die in an acute care institution. At least 55% of patients would receive hospice services before death from cancer, and less than 8% of those would be admitted to hospice within only 3 days of death. All measures were found to have accuracy ranging from 85 to 97% and 2- to 5-fold adjusted variability between the 5th and 95th percentiles of performance. CONCLUSIONS: The usefulness of these measures will depend on whether the concept of intensity of care near death can be further validated as an acceptable and important quality issue among patients, their families, health care providers, and other stakeholders in oncology.
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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 | 0.040 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".