Lessons to Improve Quality in Oncology Practice: Road Map to Fill the Global Gaps
Bibliographic record
Abstract
Abstract Oncology is a medical branch devoted to the study, diagnosis, treatment, and prevention of cancer. Cancer prevalence is increasing. By 2030, the global cancer burden is expected to grow to 21.7 million new cases and 13 million deaths. Developing as well as developed nations have cancer burden, but there is a gap. Ideally, cancer management involves a multidisciplinary team composed of qualified individuals from different specialties collaborating to optimize the care. This team must follow evidence-based medicine principles, considering three questions: What is the problem? How can we manage it? And why are we selecting this pathway? To fill the gaps in care, we present 10 questions that can help those who are managing patients with cancer globally. We concluded that although there is no “one-size-fits-all” approach, adhering to basic principles can help guide provision of evidence-based patient-centered care and fill some of the gaps in oncology.
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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 | 0.080 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".