Bibliographic record
Abstract
Supportive care encompasses the direct and treatment-related impacts of cancer, including the management of pain and other symptoms, and the psychosocial context of cancer, including spirituality and the challenges of caregiving. Patients and families endorse these concerns as critical aspects of health-related quality of life (HRQOL), and the need to provide excellent supportive care is relevant and important in all phases of cancer care. As earlier diagnosis and more effective treatments extend the experience of patients living with cancer or as disease-free survivors, seamless integration of supportive principles and approaches becomes even more imperative. Improving supportive cancer care is justifiably a priority, because cancer and its complications are associated with tremendous human suffering, because treatment typically holds the potential to harm as well as benefit, and because late-stage cancer in particular consumes enormous resources. Palliative care shares a focus on HRQOL, and is expanding rapidly in the United States and elsewhere. Palliative care is often hospital based and includes various service delivery models; however, limited evidence informs how such services, including hospice, can best serve cancer patients. Fortunately, the published literature includes a lot of evidence about what clinical interventions (eg, opioids for cancer pain), as opposed to service models (eg, opioids initiated by oncologists v palliative nurses) improve aspects of the HRQOL of cancer patients. Palliative care is often implemented late in patients’ care, but the clinical toolbox of palliative care may have much to offer patients and families with earlier-stage illness, and innovative programs integrate palliative services throughout the cancer chronology. Patients and families will be best served when oncology, palliative care, and the services that cancer patients need—including surgery and primary care—are fully integrated in various settings that cancer patients rely on. To illustrate using a recent example of an analysis of the outcomes of intensivist care from a large database, it is unfortunate that after decades of investment, fundamental questions persist as to how to best organize medical intensive care unit services. There is troubling evidence that many health care innovations are not sustained. Indeed, we might foster innovative programs and invest extensively in supportive care resources without improving HRQOL of the broad population of cancer patients and families. How can we abet the translation of relevant science into care improvement, promote programmatic development from the outset to best serve patients and families, and also ensure that supportive care improvements are sustained? An important and necessary solution to guide appropriate investment in supportive cancer services including palliative care is an accelerating focus on quality of care that transcends disciplinary boundaries. Quality of care has been defined as “the degree to which health services for individuals or populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.” In the case of supportive cancer care, that means that the focus of quality should be on helping patients achieve a higher HRQOL and helping providers deliver on processes of care (eg, prophylactic anti-emetics when the patient is at high risk for nausea and vomiting) that promote better HRQOL. In addition to outcomes or processes, quality can also focus on structure (eg, the presence of interdisciplinary providers in oncology clinics). If we take a societal view, we might consider limiting inappropriate care as an aspect of quality. Types of utilization may be harmful, and patients and families support the conceptual relationship between excess utilization and quality (eg, frequent late-life emergency department visits). This issue highlights the nascent capacity to identify which supportive issues are most important to patients and families, and which interventions will help them achieve a better HRQOL from the time of a cancer diagnosis. It also offers an emerging set of tools to evaluate the quality of supportive cancer care. Resource allocation and innovation should be driven by information about what current approaches are achieving results for patients and families, and concordance between our clinical practices and high-quality supportive care is likely to grow in importance to payors. We need to muster our collective will to ensure that developing tools reflect appropriate patientand familycentered concerns as well as the best clinical and professional input. These tools and other incentives must be appropriately tested, and they should become a focus for directing the resources to support continuous quality improvement. This issue features some important developments related to the quality of supportive cancer care in the United States, Canada, Australia, Europe, and Japan, and intentionally includes reports addressing supportive oncology care in general oncology and palliative settings. Recent efforts to improve supportive and palliative care, establish guidelines, and standardize practice are reviewed by Ferrell et al, who highlight some of the implications for practice, research, and professional education. American Society of Clinical Oncology’s Quality Oncology Practice Initiative (QOPI) focuses on developing and testing routinely JOURNAL OF CLINICAL ONCOLOGY O V E R V I E W VOLUME 26 NUMBER 23 AUGUST 1
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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 | 0.065 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".