A cancer survivorship model for holistic cancer care and research
Bibliographic record
Abstract
Advancements in cancer have increased survival rates leading to a paradigm shift such that cancer is considered a chronic disease, necessitating an evaluation of our understanding of cancer survivorship (CS). For this purpose, a comprehensive literature search was performed, using CINAHL, MEDLINE, and PUBMED from 2000-2021. Drawing from the concepts in the literature, salient factors that affect CS across cancer populations were identified and a proposed model was developed. This paper describes the Cancer Survivorship Model (CSM). The CSM represents predisposing factors for survivors and survivorship's acute, extended, and long-term phases, influencing factors: treatment and maintenance (medical/ psychosocial care), well-being, influencing aspects (life-changing experience, uncertainty, prioritizing life, wellness management, and collateral damage), and social relationship factors that impact survivors' symptom burdens and overall survivorship experience (health outcomes and quality of life). A case study demonstrates the CSM utility. Future application of the model holds promise for improving the quality of survivorship and informing research and clinical practice to promote and optimize survivors' outcomes throughout the evolving survivorship.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".