Costs of managing anemia with erythropoiesis‐stimulating agents during hemodialysis: A time and motion study
Bibliographic record
Abstract
Use of erythropoiesis-stimulating agents (ESAs) presents a significant time and cost burden in the management of anemia of chronic kidney disease (CKD). We conducted a prospective, observational, activity-based costing study to estimate the health care personnel time and resulting direct medical costs associated with administering epoetin 3 times weekly to patients with end-stage renal disease on dialysis. The study was conducted at 5 US hemodialysis centers. The personnel time and costs were derived from time and motion observations. Predicted time and cost savings were modeled for switching patients to once-monthly ESA therapy. Patients also completed a survey questionnaire to assess their level of CKD knowledge and information needs. Total per-patient-per-year (PPPY) time expended on anemia management with epoetin averaged 608 minutes (range 512-915 minutes), with an average PPPY cost of $548 (range $342-$651). Use of a once-monthly ESA, compared with epoetin, could decrease average PPPY time expenditure by 79% (127 minutes [range 96-173 minutes]) and reduce PPPY costs by 81% ($104 [range $79-$136]). The patient questionnaire reported insufficient education on CKD. Use of a once-monthly ESA to correct anemia in dialysis patients may provide substantial time, resource, and cost savings compared with current treatment practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".