The Peter Brojde Lung Cancer Centre: A Model of Integrative Practice
Bibliographic record
Abstract
BACKGROUND: The generally poor prognosis and poor quality of life for lung cancer patients have highlighted the need for a conceptual model of integrative practice. Although the philosophy of integrative oncology is well described, conceptual models that could guide the implementation and scientific evaluation of integrative practice are lacking. PURPOSE: The present paper describes a conceptual model of integrative practice in which the philosophical underpinnings derive mainly from integrative oncology, with important contributions from Traditional Chinese Medicine (TCM) and the discipline of nursing. The conceptual model is described in terms of its purpose, values, concepts, dynamic components, scientific evidence, clinical approach, and theoretical underpinnings. The model argues that these components delineate the initial scope and orientation of integrative practice. They serve as the needed context for evaluating and interpreting the effectiveness of clinical interventions in enhancing patient outcomes in lung cancer at various phases of the illness. Furthermore, the development of relevant and effective integrative clinical interventions requires new research methods based on whole-systems research. An initial focus would be the identification of interrelationship patterns among variables that influence clinical interventions and their targeted patient outcomes.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".