The Serious Illness Care Program in Oncology: Evidence, Real-World Implementation and Ongoing Barriers
Bibliographic record
Abstract
The Serious Illness Care Program (SICP), designed by Ariadne Labs, is a multicomponent intervention to improve conversations about values and goals for patients with a life-limiting illness. In oncology, implementation of the SICP achieved more, earlier, and better-quality conversations and reduced anxiety and depression among patients with advanced cancer. In this commentary, we describe the SICP, including results from the cluster-randomized trial, provide examples of real-world implementation of this program, and highlight ongoing challenges and barriers that are preventing widespread adoption of this intervention into routine practice. For the SICP to be successfully embedded into routine patient care, it will require significant effort, including ongoing leadership support and training opportunities, champions from all sectors of the interdisciplinary team, and adaptation of the program to a wider range of patients. Future research should also investigate how early conversations can be translated into personalized care plans for patients.
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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 | 0.136 | 0.421 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".