Personalized Integrative Oncology: Targeted Approaches for Optimal Outcomes
Bibliographic record
Abstract
The 11th International Conference of the Society for Integrative Oncology (SIO) brought together more than 300 clinicians, researchers, patients, and advocates to hear and interact with world-leading experts about the latest research in the areas of nutrition, exercise, acupuncture, health services research, meditation, and other integrative disciplines. The conference theme, "Personalized Integrative Oncology: Targeted Approaches for Optimal Outcomes," highlighted innovations in personalized medicine and ways this growing field will advance the evolution of individualized integrative cancer care to the next level. This year's conference also featured a clinical track focusing on clinical information for the practicing health care professional. The conference's rigorous schedule included 3 keynotes, 4 plenary sessions, 2 interdisciplinary tumor boards, 5 workshops, 45 concurrent oral sessions, and 106 posters. In addition to the conference theme, keynote and plenary sessions presented topics on stress and cancer, the importance of sleep for cancer patients, epigenetic mechanisms of lifestyle and natural products, recently published Journal of the National Cancer Institute monograph on integrative oncology, SIO's clinical practice guidelines for breast cancer survivors, and a joint session of the American Academy of Hospice and Palliative Medicine and SIO about supportive care and symptom management. This highly successful conference helped further the mission of the SIO to advance evidence-based, comprehensive, integrative health care to improve the lives of people affected by cancer.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".