Dialysis‐related practice patterns among hemodialysis patients with cancer
Bibliographic record
Abstract
Abstract Rationale, aims, and objectives With the achievement of longevity in hemodialysis patients, the risk of comorbid cancer has begun to draw attention. In the present study, we examined dialysis‐related practice patterns and compared those patterns by cancer status. Methods Using data from the Japan Dialysis Outcomes and Practice Patterns Study phase 4, we evaluated 2153 hemodialysis patients. Baseline cancer status for patients was separated into 3 categories: no cancer, cancer with recent treatment, and cancer without recent treatment. We then assessed variations among hemodialysis patients in dialysis‐related practice patterns, including anemia management, management of mineral and bone metabolism disorder, nutritional management, and dialysis treatment, by cancer status. Results We observed both similarities and differences in dialysis‐related practice patterns among hemodialysis patients, by cancer status. Hemoglobin levels were largely similar for all cancer statuses, although erythropoiesis stimulating agents dose tended to be higher in hemodialysis patients with recent cancer treatment (multivariable adjusted mean difference of erythropoiesis stimulating agents dose: 5.4 × 103 IU/L/month) than in those without cancer. Phosphorus and calcium levels were also similar. Nutrition statuses were similar among cancer statuses, as were dialysis therapies. These results suggested that physicians do not modulate their dialysis‐related practices based on whether or not a hemodialysis patient has cancer. Conclusion Among long‐term facility‐based hemodialysis patients with cancer, we detected no statistically significant differences to suggest that cancer status affects hemodialysis practice regarding mineral and bone disorder management, nutritional management, and dialysis treatment. Facility‐based hemodialysis patients with recent cancer treatment, however, receive a higher dose of erythropoietin‐stimulating agent than those without cancer.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".