A Decade of Research on the Quality of Systemic Cancer Therapy in Routine Care: What Aspects of Quality Are We Measuring?
Bibliographic record
Abstract
PURPOSE: To systematically review population-based studies that evaluated the quality of care related to delivery of systemic cancer therapy (ST) in routine practice, summarize the literature, and define knowledge gaps across five quality domains: access, treatment delivery, toxicity, safety, and outcome. METHODS: We searched MEDLINE using terms pertaining to ST and keywords related to health care quality for articles published in English from January 1, 2000, to December 31, 2010. Articles were included if they were original studies that examined quality of care of ST among adult patients with cancer from a population perspective. RESULTS: Our search identified 179 articles. The number of studies published each year increased over time. Most studies (58%) were conducted in the United States in either colorectal (31%) or breast cancers (27%), and focused on adjuvant intent cytotoxic chemotherapy (73%). The majority of the studies (92%) retrospectively identified patients from cancer registries (83%) and used either billing data (36%) or information in the registry itself (27%) for identification of ST. No study examined safety from a population perspective. Access was the most frequently evaluated domain (77%), whereas treatment delivery was the least examined (12%). Among studies that assessed the outcome of ST, most evaluated patient-specific outcomes such as survival (89%), although a few (11%) examined system-level outcomes such as cost. CONCLUSION: The majority of studies evaluating quality of ST have focused on access to cytotoxic chemotherapy in early stage disease. Further studies focusing on other aspects of quality and in different clinical settings are needed.
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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 | 0.072 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.013 | 0.024 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".