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Costs of managing anemia with erythropoiesis‐stimulating agents during hemodialysis: A time and motion study

2008· article· en· W2012766458 on OpenAlexvenueno aff
Brigitte Schiller, Sheila Doss, E De Cock, Michael A. del Aguila, Allen R. Nissenson

Bibliographic record

VenueHemodialysis International · 2008
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsnot available
FundersU.S. Department of Labor
KeywordsMedicineHemodialysisAnemiaKidney diseaseDialysisObservational studyErythropoiesisEpoetin alfaTarget rangeEmergency medicineIntensive care medicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.267
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2008
Admission routes1
Has abstractyes

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