Principles to Guide Integrative Oncology and the Development of an Evidence Base
Bibliographic record
Abstract
BACKGROUND: Integrative oncology uses both conventional and complementary medicine to meet the needs of individual patients and to focus on the whole person. The core principles of integrative oncology include individualization, holism, dynamism, synergism, and collaboration, but the nature of the evidence to guide the development of integrative oncology has been given little attention. OBJECTIVES: To discuss the need for evidence to support the integration of complementary therapies for integrative oncology care. To emphasize that the evidence base must be valid and respect the underlying principles of individual complementary therapies and integrative oncology practice. To suggest ways to begin developing the evidence base. REVIEW AND DISCUSSION: Although the evidence for safety and efficacy seems paramount for supporting the integration of an individual complementary therapy into mainstream cancer care, the need for evidence to support the overall practice of integrative oncology has to be considered as well. We argue that developing an evidence base for integrative oncology requires a contextual and comprehensive research approach that assesses a range of outcomes over a suitable period of time that the patient and the patient's family, in addition to the health care providers, deem important. CONCLUSION: A whole-systems framework to the development of the evidence base for integrative oncology can guide the development of evidence that respects the complex nature of many complementary and integrative practices and their underlying principles of care delivery.
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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 | 0.363 | 0.348 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.026 | 0.013 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.014 | 0.019 |
| Research integrity | 0.022 | 0.044 |
| Insufficient payload (model declined to judge) | 0.006 | 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".