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Record W2025454845 · doi:10.5301/ijao.5000186

Use of Electron-Beam Sterilized Hemodialysis Membranes is Not Associated with Significant and Persistent Thrombocytopenia

2013· article· en· W2025454845 on OpenAlexaboutno aff
Vassilis Filiopoulos, Nikolaos Manolios, Dimosthenis Vlassopoulos

Bibliographic record

VenueThe International Journal of Artificial Organs · 2013
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisMedicineDialysisPlateletMembraneSterilization (economics)Significant differenceSurgeryInternal medicineChemistry

Abstract

fetched live from OpenAlex

Significant thrombocytopenia following hemodialysis with electron-beam (e-beam) sterilized membranes has been recently reported in a large-scale Canadian study. However, the underlying mechanism and the clinical significance of this finding remain undetermined as yet. We prospectively evaluated for a 4-month period the thrombocytopenic effect of the e-beam sterilized dialyzers as compared to the steam sterilized ones in two groups of well-controlled hemodialysis patients. There were no significant differences in pre- and post-dialysis platelet counts of patients using e-beam-sterilized dialyzers compared to those using steam-sterilized ones. Furthermore, no statistically significant differences between pre- and post-dialysis platelet counts were found throughout the study in the e-beam-sterilized group. However, 1 out of 9 patients demonstrated significant post-dialysis thrombocytopenia in 2/4 measurements. In conclusion, our study data do not support a continuously occurring thrombocytopenic effect in patients dialyzed with e-beam sterilized membranes. Further studies are needed to determine whether and how the use of e-beam sterilization may impact the interaction between certain membranes and platelets.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.032
GPT teacher head0.255
Teacher spread0.223 · 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 designBench or experimental
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

Citations6
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe International Journal of Artificial OrgansSame topicDialysis and Renal Disease ManagementFrench-language works237,207