Integrative Oncology Outpatient Consultations: Long-Term Effects on Patient-Reported Symptoms and Quality of Life
Bibliographic record
Abstract
Background: Integrative oncology (IO) seeks to bring non-conventional approaches into conventional oncology care in an evidence-based, coordinated manner. Little is known about the effects of such consultations on patient-reported symptoms. Methods: We reviewed data from patients referred for an IO outpatient consultation between 2009 and 2013, comparing the cohort of patients with at least one follow-up to the cohort with an initial consultation only. Assessments completed at initial and follow-up encounters included: complementary and alternative medicine (CAM) use questionnaire, Measure Yourself Concerns and Wellbeing (MYCaW), Edmonton Symptom Assessment Scale (ESAS; 10 symptoms, scale 0-10, 10 worst), and post-consultation satisfaction. ESAS individual items and global (GDS; score 0-90), physical (PHS, 0-60) and psychological (PSS, 0-20) distress scales were analyzed. Results: 642 patients out of 2,474 (26%) new patient IO consultations had at least one follow-up encounter (mean 3.2; SD 1.8). Age, place of residence, and higher satisfaction were predictors of follow-up. Statistically significant improvement in symptoms between initial consult and follow-up were observed for depression, anxiety, well-being, and subscales of GDS and PSS (all p's > 0.01). For those with moderate to severe symptoms at their initial consult (ESAS scores 4), we observed clinical response rates (improvement) of 49-75% for all ESAS symptoms at follow-up. Conclusions: Patients presenting for IO follow-up had overall mild to moderate symptoms at baseline and stable symptom burden over time. Greatest improvements were observed for psychosocial symptoms, most pronounced for the subset of patients with moderate to severe symptoms at their initial consultation.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".