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Financial Aspects of Renal Replacement Therapy in Acute Kidney Injury

2011· review· en· W1955588051 on OpenAlexaff
Matthew T. James, Marcello Tonelli

Bibliographic record

VenueSeminars in Dialysis · 2011
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsInstitute of Health EconomicsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineRenal replacement therapyDialysisIntensive care medicineHemodialysisAcute kidney injuryRandomized controlled trialPsychological interventionRenal functionKidney diseaseEmergency medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) is associated with high morbidity and mortality and consumes substantial health-care resources, particularly when renal replacement therapy is required. Randomized controlled trials (RCTs) have not identified the optimal mode of renal replacement for AKI in terms of clinically relevant endpoints such as patient survival or recovery of renal function. As for other complex health interventions, the costs and consequences of AKI treatment are relevant to health-care providers and decision makers aiming to maximize health outcomes despite fixed health resources. Studies from several different centers suggest that continuous renal replacement therapy (CRRT) is more costly than intermittent hemodialysis and less economically attractive than even intensive intermittent dialysis. On the other hand, while the incremental costs of providing CRRT are significant, they remain relatively small compared with the projected costs of providing chronic dialysis to survivors who do not recover renal function. Even small differences in the risk of chronic dialysis in survivors are likely to determine the economic attractiveness of the different types of renal replacement therapies. To clarify the true incremental cost-effectiveness of these therapies, future RCTs should collect data on long-term survival, the need for chronic dialysis, and detailed information on costs.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.374
Teacher spread0.330 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2011
Admission routes1
Has abstractyes

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