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Record W4380626301 · doi:10.1093/ndt/gfad063c_3286

#3286 ENVIRONMENTAL PERFORMANCE OF HEMODIALYSIS THROUGH LIFECYCLE ASSESSMENT (LCA): IN-CENTRE HEMODIALYSIS VS HOME-HEMODIALYSIS

2023· article· en· W4380626301 on OpenAlexaffabout
Saba Saleem, Tasleem Rajan, Andrea J. MacNeill, Caroline Stigant, Michael A. Copland, Kasun Hewage, Rehan Sadiq, Christopher Nguan

Bibliographic record

VenueNephrology Dialysis Transplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHemodialysisMedicineHome hemodialysisLife-cycle assessmentMedical prescriptionDialysisEnvironmental impact assessmentSurgeryNursingProduction (economics)

Abstract

fetched live from OpenAlex

Abstract Background and Aims Despite being energy and resource-intensive, hemodialysis (HD) is the most common therapy for end stage kidney disease. Considering the extensive amount of energy, water, and consumables involved, this modality is expected to have a significant environmental impact. Through a comparative lifecycle assessment (LCA), this study aims to analyze the environmental impacts of hemodialysis in British Columbia, Canada. Method A process-based life cycle assessment was performed for three hemodialysis modalities: i) in-centre HD (ICHD), ii) home HD with NxStage machine (HHD-Nx), and iii) home HD with Baxter AK-98 machine (HHD-B). The scope of study included patient and staff commute, supply transportation, dialysis services, patient training for home hemodialysis, and waste management. The functional unit considered is HD energy and material consumption for one patient per year. Based on the patient's record, two prescriptions including standard (three dialysis sessions per week for 4 hours daily) and extended (six HD sessions per week for 8 hours daily) were considered. LCAs were performed using ISO 14040, 14041 standards in which ReCiPe (world) midpoint method and a cut-off criterion of 0.5% was used to assess the environmental impacts of selected impact categories. Results For both prescriptions, ICHD had highest impact on all environmental impact categories (climate change, human toxicity, freshwater eutrophication, particulate matter formation, marine ecotoxicity and water depletion), except ozone depletion in which HHD-B had highest impact. The fewest impacts were associated with HHD-Nx. CO2 emissions observed for a standard HD prescription are 3590 kg carbon dioxide equivalents (CO2eq) per patient/year by ICHD, 1350 kg CO2eq/patient/year by HHD-B and 733 kg CO2eq/patient/year by HHD-Nx. For extended prescriptions, the highest impact observed is from HHD-B (2210 kg CO2eq/patient/year) followed by HHD-Nx (1110 kg CO2eq/patient/year). With respect to ICHD, HHD-Nx only accounts for 13% (standard) and 18% (extended) of total environmental impact. Patient and staff commute in ICHD (40% of total impact) and dialysis services in HHD (75% in HHD-B and 63% in HHD-Nx) are the highest contributors to the majority of environmental impacts. Conclusion Our study demonstrates the environmental impacts of various modes of HHD through LCA. HD modalities have substantial differences in their environmental impacts as compared to ICHD, both HHD systems have lower impact, with NxStage having less impact than Baxter. Our results also demonstrate that a shift from standard to extended prescriptions increases overall environmental impact by 5%. Combined with existing clinical and economic data, these results could assist policy and decision-makers to optimize the provision of kidney replacement therapies. Lower environmental impact of HHD may add to the patient and provider appeal of these therapies, and when clinical equipoise exists, NxStage may be preferred over Baxter in eligible patients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.464

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.246
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2023
Admission routes2
Has abstractyes

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