Real-World Approaches to Quality Improvement in Oncology
Bibliographic record
Abstract
Oncologists face increasing complexity in the delivery of high-quality cancer care.Challenges arise from the simultaneous rapid expansion of the armamentarium of anticancer treatments with substantive changes in payment delivery that emphasize value.We increasingly appreciate the role of the oncologist to improve care across the cancer continuum, from prevention through survivorship and end-of-life care.Also, with a focus on patient centeredness, shared decision making, and quality of life, we appreciate the role of the oncologist as broader than selection and delivery of treatment.Such pressures require oncologists to remain continuously apprised of best practices and evidence, to regularly engage with data to identify care gaps that do not align with recommendations, and then to adeptly apply quality improvement skillsto evolve processes and drive behavior change.As we recognize that possibilities and standards in cancer care are quickly evolving, we appreciate that high-quality care is not a destination but an iterative journey.Conversely, the broad shifts in care themselves challenge oncologists to be practical in affecting change.Doing more with less is the frequent modus operandi, and time is consistently the most valuable commodity.For example, one study of Canadian oncologists 1 in Journal of Oncology Practice found that, although the vast majority of oncologists (97%) believe quality improvement is important, less than half (49%) participated in a quality improvement project in the past 5 years.Time constraints were frequently cited as
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".