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Record W2516161125 · doi:10.5772/64679

Cost-Effectiveness of Online Hemodiafiltration

2016· book-chapter· en· W2516161125 on OpenAlexaboutno aff
Khalid Alsaran, Khalid B. Mirza

Bibliographic record

VenueInTech eBooks · 2016
Typebook-chapter
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisMedicineEnd stage renal diseaseCost effectivenessIntensive care medicineQuality of life (healthcare)Emergency medicineSurgeryRisk analysis (engineering)Nursing

Abstract

fetched live from OpenAlex

Care of patients with end-stage renal disease (ESRD) is essential but also resource intense. We review several studies on online hemodiafiltration (OL-HDF), which concluded that high-volume OL-HDF is associated with better outcome compared to conventional hemodialysis. The cost-effectiveness of OL-HDF was shown in many studies. For example, in the Canadian setting of the Convective Transport Study (CONTRAST), the high-efficiency OL-HDF was shown to be cost-effective compared with low-flux hemodialysis (LF-HD) for patients with ESRD. In our study (Al Saran et al.), it was shown that the cost of hemodialysis was quite less in Saudi Arabia than in other industrialized countries while maintaining a high standard of care. In our retrospective analysis of the cost of OL-HDF in the same center, it was only 3% higher than the conventional HD, which indicates that it is cost-effective considering the improved hospitalization rate, the mortality rates, and the likely better quality of life associated with it. The trend of increased practice of OL-HDF may encourage the practice of home OL-HDF as well. It has been shown that home HD is more cost-effective than in-center HD and we presume that the same results will be applied to home OL-HDF as well.

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: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.633
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.043
GPT teacher head0.305
Teacher spread0.262 · 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 designOther design
Domainnot available
GenreOther

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

Citations1
Published2016
Admission routes1
Has abstractyes

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