A Systematic Review of Integrative Oncology Programs
Bibliographic record
Abstract
OBJECTIVE: This systematic review set out to summarize the research literature describing integrative oncology programs. METHODS: Searches were conducted of 9 electronic databases, relevant journals (hand searched), and conference abstracts, and experts were contacted. Two investigators independently screened titles and abstracts for reports describing examples of programs that combine complementary and conventional cancer care. English-, French-, and German-language articles were included, with no date restriction. From the articles located, descriptive data were extracted according to 6 concepts: description of article, description of clinic, components of care, administrative structure, process of care, and measurable outcomes used. RESULTS: Of the 29 programs included, most were situated in the United States (n = 12, 41%) and England (n = 10, 34%). More than half (n = 16, 55%) operate within a hospital, and 7 (24%) are community-based. Clients come through patient self-referral (n = 15, 52%) and by referral from conventional health care providers (n = 9, 31%) and from cancer agencies (n = 7, 24%). In 12 programs (41%), conventional care is provided onsite; 7 programs (24%) collaborate with conventional centres to provide integrative care. Programs are supported financially through donations (n = 10, 34%), cancer agencies or hospitals (n = 7, 24%), private foundations (n = 6, 21%), and public funds (n = 3, 10%). Nearly two thirds of the programs maintain a research (n = 18, 62%) or evaluation (n = 15, 52%) program. CONCLUSIONS: The research literature documents a growing number of integrative oncology programs. These programs share a common vision to provide whole-person, patient-centred care, but each program is unique in terms of its structure and operational model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".