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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".