Factors Associated with Multimodal Care Practices for Cancer Cachexia among Pharmacists
Bibliographic record
Abstract
Pharmacists' roles in cachexia care are unclear. This study aimed to clarify the knowledge and practice of cachexia care and identify factors related to the practice of cachexia care among pharmacists. Information on the knowledge and practice of cachexia care was obtained. Components of practicing multimodal care were evaluated. Participants were categorized into two groups according to practicing multimodal care levels. Comparisons were made between the groups, and multiple regression analysis was employed. Of the 451 pharmacists, 243 responded. They were categorized into the Practicing group (n = 119) and Not practicing group (n = 124). Significant differences were observed for the number of advanced cancer patients/month, frequency of caring for them, and involvement in training programs on cachexia. The Practicing group had significantly better knowledge about cachexia. The Practicing group used guidelines, items, and symptoms more frequently to detect cachexia. The Practicing group tended to detect cachexia and initiate interventions in earlier phases and in patients with a better status. Multivariate logistic regression analysis showed that the most significant factor was the regular provision of care (odds ratio, 2.07; 95% confidence interval, 1.10-3.92). The regular provision of care was associated with the practice of multimodal care.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".