Factors Associated With Practice of Multimodal Care for Cancer Cachexia Among Physicians and Nurses Engaging in Cancer Care
Bibliographic record
Abstract
PURPOSE Multimodal care for cancer cachexia is needed. This study examined factors associated with practicing multimodal cachexia care among physicians and nurses engaging in cancer care. METHODS This was a preplanned secondary analysis of a survey investigating clinicians' perspectives on cancer cachexia. Data of physicians and nurses were used. Data on knowledge, skills, and confidence in multimodal cachexia care were obtained. Nine items on practicing multimodal cachexia care were evaluated. Participants were divided into two groups as practicing multimodal cachexia care (above median value for the nine items) or not. Comparisons were made using the Mann-Whitney U test or chi-square test. Multiple regression analysis was performed to identify the factors of practicing the multimodal care. RESULTS Total of 233 physicians and 245 nurses were included. Significant differences were observed between the groups: female sex ( P = .025), palliative care versus oncology specialization ( P < .001), the number of clinical guidelines used ( P < .001), the number of symptoms used ( P = .005), training for cancer cachexia ( P = .008), knowledge on cancer cachexia ( P < .001), and confidence in cancer cachexia management ( P < .001). Palliative care specialization (partial regression coefficient [ B] = 0.85; P < .001), the number of clinical guidelines used ( B = 0.44; P < .001), knowledge on cancer cachexia ( B, 0.94; P < .001), and confidence in cancer cachexia management ( B = 1.59; P < .001) were statistically significant in multiple regression analysis. CONCLUSION Specialization in palliative care, specific knowledge, and confidence were associated with the practice of multimodal care for cancer cachexia.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".