Cancer care quality measures: symptoms and end-of-life care.
Bibliographic record
Abstract
OBJECTIVES: To systematically identify quality measures and the evidence for them-to support quality assessment and improvement in the palliative care of patients with cancer in the areas of pain, dyspnea, depression, and advance care planning (ACP), and to identify important gaps in related research. DATA SOURCES: MEDLINE, CINAHL, and PsycINFO in English 1995-2005. We also conducted an extensive Internet search of professional organizations seeking guidelines and other grey literature (i.e., not published in peer-reviewed journals) using similar terms and attempted to contact all measure developers. REVIEW METHODS: We searched using terms for each domain for patients (adults and children) with a cancer diagnosis throughout the continuum of care (e.g., diagnosis to death). Pain and depression searches were limited to cancer, but we searched broadly for dyspnea and ACP, because the evidence base for dyspnea is more limited and experts advised that ACP measures would be generalizable to cancer. Measures were included if they expressed a normative relationship to quality and included a measurable numerator and denominator. Citations and articles were each reviewed/abstracted by two of six palliative care researcher/clinicians who described populations, testing, and attributes for each measure. RESULTS: The literature search identified 5,187 titles, of which 4,650 were excluded at abstract review. Of 537 articles, only 25 contained measures: 21 on ACP, 4 on depression, 2 on dyspnea, and 12 on pain. Ten relevant measure sets were identified: ACOVE, QA Tools, Cancer Care Ontario, Cancer Care Nova Scotia, Dana-Farber, Georgia Cancer Coalition, University Health Consortium, NHPCO, VHA, and ASCO. We identified a total of 40 operationalized and 19 non-operationalized measures. The most measures were available for pain (12) and ACP (21), compared with only 4 for depression and 2 for dyspnea. Few of the measures were published, and few had been specifically tested in a cancer population. CONCLUSIONS: A large number of measures are available for addressing palliative cancer care, but testing them in relevant populations is urgently needed. No measures or indicators were available to evaluate the quality of supportive pediatric cancer care. Basic research is urgently needed to address measurement in populations with impaired self-report. Funding field testing of highest quality measures should be an urgent patient and family-centered priority to meet the needs of patients with cancer.
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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 | 0.044 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".