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Record W4406844497 · doi:10.1016/j.ekir.2024.11.628

WCN25-3701 THE DESTINY OF END-OF-LIFE HEMODIALYSIS MACHINES: AN INTERNATIONAL SURVEY

2025· article· en· W4406844497 on OpenAlexaff
Andrew Davenport, M. Ben Hmida, Wisit Cheungpasitporn, Ikechi G. Okpechi, Fádi Fakhouri, Rasha Samir Shemies, Jie Dong, Mothusi Walter Moloi, Andrew Mallett, Jane Waugh, Alejandra Orozco-Guillén, Carmen Coroban, G Piccoli, Massimo Torreggiani

Bibliographic record

VenueKidney International Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDestiny (ISS module)MedicineHemodialysisIntensive care medicineInternal medicineEngineering

Abstract

fetched live from OpenAlex

Approximately one million hemodialysis (HD) machines are in operation worldwide, and some 100,000 are discarded each year. In Europe, legislation imposes a maximum lifespan – in France between 10 and 12 years – for HD machines. HD machines contain electronics parts and must be managed as Waste from Electrical and Electronic Equipment (WEEE). WEEEs are often sent to low- and middle-income countries (LMICs): the Agbogbloshie area in Dacca, Ghana, is an example of e-waste mismanagement with a significant impact on the local population.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.114
GPT teacher head0.440
Teacher spread0.326 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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