Whole person integrative cancer care in action - an overview of its implementation and patient related outcomes
Bibliographic record
Abstract
Learning Objectives: Explore and understand the implementation and evaluation of an integrative cancer care model based on evidence based care and programming. Goals of care are to improve quality of life for those with cancer and work towards cancer prevention through a combination of approaches including Complementary medicine including nutrition, exercise and lifestyle management.Background and Project Objectives:The Ottawa Integrative Cancer Centre (OICC) provides evidence-informed integrative and preventative cancer care, research, and education across the spectrum of prevention to survivorship. The purpose of this presentation is to describe the population receiving care at the OICC in terms of demographic and disease-related characteristics, in addition to their experience receiving care in terms of therapies received and preliminary outcomes.Methods: All people receiving care at the OICC are invited to complete a registration package that documents demographic and disease-related characteristics in addition to baseline quality of life (EORTC-QLQ C30), cancer-related symptom (ESAS) and patient-identified concerns or problems (MYCaW). Every 3 months, a follow up assessment is completed for continuing patients.Results:The majority of people seek care at the OICC to manage cancer and treatment related side effects. Other people seek supportive care with their prescribed standard care, to control or cure their cancer, or to prevent a recurrence. Approximately one half of people self-identify as under distress for at least one common cancerrelated symptom. Most patients consult a naturopathic doctor, often in combination with a general practitioner, nutritionist, acupuncturist, physiotherapist, or counselor.Results will be presented regarding quality of life, cancer related symptoms and patient-identified concerns, stratified as possible by age, sex, cancer type, stage and types of treatment received.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".