Implementing Evidence-Based Palliative Care Programs and Policy for Cancer Patients: Epidemiologic and Policy Implications of the 2016 American Society of Clinical Oncology Clinical Practice Guideline Update
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The American Society of Clinical Oncology (ASCO) recently convened an Ad Hoc Palliative Care Expert Panel to update a 2012 provisional clinical opinion by conducting a systematic review of clinical trials in palliative care in oncology. The key takeaways from the updated ASCO clinical practice guidelines (CPGs) are that more people should be referred to interdisciplinary palliative care teams and that more palliative care specialists and palliative care-trained oncologists are needed to meet this demand. The following summary statement is based on multiple randomized clinical trials: "Inpatients and outpatients with advanced cancer should receive dedicated palliative care services, early in the disease course, concurrent with active treatment. Referral of patients to interdisciplinary palliative care teams is optimal, and services may complement existing programs" (J Clin Oncol. 2017;35(1):96). This paper addresses potential epidemiologic and policy interpretations and implications of the ASCO CPGs. Our review of the CPGs demonstrates that to have clinicians implement these guidelines, there is a need for support from stakeholders across the health-care continuum, health system and institutional change, and changes in health-care financing. Because of rising costs and the need to improve value, the need for coordinated care, and change in end-of-life care patterns, many of these changes are already underway.
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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 | 0.028 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it