Claims/utilization-based intensity of end-of-life (EOL) cancer care in integrated health systems (IHS).
Bibliographic record
Abstract
230 Background: Little is known about the intensity of EOL cancer care for patients within IHS or those under 65 years of age. This study assessed achievement of National Quality Forum (NQF) benchmarks for EOL cancer care among IHS enrollees aged >20 at death. Methods: Using methods described by Earle, we identified IHS enrollees whose cause of death was cancer (not diagnosed at death or via autopsy) per tumor registry data between 2000-2008. Using claims/utilization data we identified individuals who received chemotherapy (chemo) in the last 14 days of life (C, benchmark (B): <10%) or a new chemo regimen in the last 30 days of life (NC, B<2%); had >1 emergency department visit (ER, B<4%):) or hospitalization (H, B<4%) in the last 30 days of life; died in hospital (HD, B: <17%), were not admitted to hospice (NH, B: <45%), or where admitted to hospice in the last 3 days of life (SH, B: <8%). The percentages were then compared to previously established fee-for-service (FFS) Medicare benchmarks. Results: At death, 4,924 IHS cancer patients were >65 years of age, 3,478 were <65. For those >65, benchmarks were achieved for chemo in the last 14 days of life (5%), death in an acute care hospital (16%), and hospice stay <3 days (4%). For those aged 21-64 years of age at death, benchmarks were achieved for chemo in the last 14 days of life (7%) and hospice stay <3 days (4%). Benchmarks were not achieved in either group for new chemo regimens, ER visits, or hospitalizations in the last 30 days of life or hospice admissions. Conclusions: Most NQF benchmarks for EOL cancer care were not achieved, particularly among the young. Future research should update FFS benchmarks based on current utilization and clarify benchmarks for those <65. Final presentation will include results from at least 2 other IHS. [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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".