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Record W2141602647 · doi:10.1017/s0266462309990134

Economic evaluation of continuous renal replacement therapy in acute renal failure

2009· article· en· W2141602647 on OpenAlexaff
Scott Klarenbach, Braden Manns, Neesh Pannu, Fiona Clement, Natasha Wiebe, Marcello Tonelli

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2009
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsRenal replacement therapyMedicineIntensive care medicineUrology

Abstract

fetched live from OpenAlex

OBJECTIVES: Controversy exists regarding the optimal method of providing dialysis in critically ill patients with acute renal failure. We sought to determine the cost-effectiveness of treatment strategies. METHODS: Adult subjects requiring renal replacement therapy in a critical care setting who are candidates for intermittent hemodialysis (IHD) or continuous renal replacement therapy (CRRT) were considered within a Markov model. Alternative strategies including IHD, and standard or high dose CRRT were compared. The model considered relevant clinical and economic outcomes, and incorporated data on clinical effectiveness from a recent systematic review and high quality micro-costing data. RESULTS: In the base-case analysis, CRRT was associated with similar health outcomes but higher costs by ($3,679 more than IHD per patient). In scenarios considering alternate cost sources, and higher intensity of IHD (including daily and longer duration IHD), CRRT remained more costly. Sensitivity analysis indicated that even small differences in the risk of mortality or need for long-term chronic dialysis therapy among surviving patients benefits led to dramatic changes in the cost-effectiveness of the modalities considered. CONCLUSIONS: Given the higher costs of providing CRRT and absence of demonstrated benefit, IHD is the preferred modality in critically ill patients who are candidates for either IHD or CRRT, although this conclusion should be revisited if future clinical trials establish differences in clinical effectiveness between modalities. Future interventions that are proven to improve renal recovery after acute renal failure are likely to be cost-effective, even if very resource intensive.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.449
Teacher spread0.423 · 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

Citations69
Published2009
Admission routes1
Has abstractyes

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