Understanding Weight Loss in Patients With Colorectal Cancer: A Human Response to Illness
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To provide a comprehensive overview of weight loss in patients with colorectal cancer (CRC) within the context of the Human Response to Illness (HRTI) model. DATA SOURCES: Research from 1990-2008 and classic research from the 1980s were included. PubMed, CINAHL(R), and Google Scholar were searched for the terms cancer, CRC, weight loss, and cancer cachexia. DATA SYNTHESIS: Progressive, unintentional weight loss is a common issue in patients with CRC that has a devastating effect on patients' self-image, quality of life, and survival. Physiologic abnormalities, responses to the tumor, and treatments contribute to weight loss in these patients. In addition, cancer cachexia is an end-stage wasting syndrome and a major cause of morbidity and mortality in this population. CONCLUSIONS: The HRTI model provides an appropriate framework to gain a comprehensive understanding of the physiologic, pathophysiologic, behavioral, and experiential perspectives of weight loss and cancer cachexia in patients with CRC. IMPLICATIONS FOR NURSING: By examining weight loss in patients with CRC within the context of the four perspectives of the HRTI model, oncology and gastroenterology nurses can gain insight into optimal, evidence-based assessment and management of this patient population. In addition, current gaps in knowledge can be identified and provide guidance for future nursing research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".