Toward a holistic understanding of cancer cachexia: Application of the human response to illness model
Bibliographic record
Abstract
Cachexia is a commonly presenting multidimensional syndrome in individuals living with advanced cancer. Given its prevalence of between 50% and 80%, nurses are going to encounter individuals manifesting ongoing loss of skeletal muscle mass (with or without loss of fat mass) that can be partially but not entirely reversed by conventional nutritional support. Thus nurses require a comprehensive understanding of this complex clinical problem. Research suggests, however, that nurses receive minimal education about cachexia or its management. Limited understanding undermines the ability to confidently care for patients with cachexia and their families, thereby hampering effective practice. The human response to illness model provides nurses with an organizing framework to guide and make sense of their assessments in clinical practice when caring for patients with cancer cachexia and provides direction for appropriate intervention. This article illustrates the integration of the human response to illness model to clinical practice, thereby assisting nurses to develop a comprehensive understanding of the physiological, pathophysiological, behavioral, and experiential facets of cachexia in advanced cancer patients. Contemporary areas of further interest and research will be presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".