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Hemodialysis cost in Tehran, Iran

2008· article· en· W2057878068 on OpenAlexvenueno aff
Mitra Mahdavi‐Mazdeh, Mozafar Zamani, Mahnaz Zamyadi, Hamid Rajolani, Keyvan TAJBAKHSH, Alireza Heidary Rouchi, Mahammad AGHIGHI, Mahdavi Azita

Bibliographic record

VenueHemodialysis International · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisPeritoneal dialysisTotal costRenal replacement therapyDialysisIndirect costsEmergency medicineIntensive care medicineSurgeryBusiness

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the health service cost of hemodialysis (HD) delivered at hospitals in Iran as a developing country with a well-defined program of renal replacement therapy. A cost analysis was performed from the viewpoint of the 2 hospitals, with 3 shifts and full chairs, on current practice for dialysis maintenance. Cost and patient data were collected in 2006 and from April 1 to May 31, 2007, respectively. A total of 22,464 HD sessions were performed and 247 patients were studied during the study period. The reference year for the value of USD for different mentioned costs was 2006. Health care sector costs associated with each HD session were estimated at US$78.87. Most of the total maintenance expenditure was made up of medical supplies (36.19%), with dialyzers as the major cost driver. Staff salaries represented 17% of the cost and fixed direct capital costs accounted for 21.4%. Of the family members, 32.4% accompanied their patients. The mean cost for transportation of patients and accompanied person was US$3.15 +/- 2.83 and US$1.5 +/- 0.29, respectively. These findings are important in the light of limited available resources coupled with the increasing prevalence of kidney failure. A major attempt should also be made to increase peritoneal dialysis coverage as in some centers we cannot keep all chairs full, especially in some vast areas. It is highly recommended to place initial focus on strategies and treatments that slow disease progression, to postpone renal replacement therapy to save resources.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.065
GPT teacher head0.266
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

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

Citations23
Published2008
Admission routes1
Has abstractyes

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