Quality indicators for systemic anticancer therapy services: a systematic review of metrics used to compare quality across healthcare facilities
Bibliographic record
Abstract
PURPOSE: The number of systemic anticancer therapy (SACT) regimens has expanded rapidly over the last decade. There is a need to ensure quality of SACT delivery across cancer services and systems in different resource settings to reduce morbidity, mortality, and detrimental economic impact at individual and systems level. Existing literature on SACT focuses on treatment efficacy with few studies on quality or how SACT is delivered within routine care in comparison to radiation and surgical oncology. METHODS: Systematic review was conducted following PRISMA guidelines. EMBASE and MEDLINE were searched and handsearching was undertaken to identify literature on existing quality indicators (QIs) that detect meaningful variations in the quality of SACT delivery across different healthcare facilities, regions, or countries. Data extraction was undertaken by two independent reviewers. RESULTS: This review identified 63 distinct QIs from 15 papers. The majority were process QIs (n = 55, 87.3%) relating to appropriateness of treatment and guideline adherence (n = 28, 44.4%). There were few outcome QIs (n = 7, 11.1%) and only one structural QI (n = 1, 1.6%). Included studies solely focused on breast, colorectal, lung, and skin cancer. All but one studies were conducted in high-income countries. CONCLUSIONS: The results of this review highlight a significant lack of research on SACT QIs particularly those appropriate for resource-constrained settings in low- and middle-income countries. This review should form the basis for future work in transforming performance measurement of SACT provision, through context-specific QI SACT development, validation, and implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".