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Record W2076313780 · doi:10.1002/jca.21221

Plasma exchange for renal disease: Evidence and use 2011

2012· review· en· W2076313780 on OpenAlexaffabout
William F. Clark

Bibliographic record

VenueJournal of Clinical Apheresis · 2012
Typereview
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsLondon Health Sciences CentreLawson Health Research InstituteWestern University
Fundersnot available
KeywordsMedicineApheresisPlasmapheresisCryoglobulinemiaNephropathyDiseaseInternal medicineIntensive care medicineGastroenterologyImmunologyEndocrinologyDiabetes mellitusPlatelet

Abstract

fetched live from OpenAlex

Over the past 37 year the role of plasma exchange in the treatment of patients with renal disease has undergone several changes. The majority of the changes for the use of plasma exchange relied on randomized control trials and delineations of mechanisms that potentially would benefit from the use of plasma exchange. Over the past 11 years plasma exchange indications for renal disease, the absolute numbers have been relatively unchanged but the indications are quite different. The Canadian Apheresis Group indicated in 2010 that TTP/HUS is still the number 1 indication at 63% of the total plasma exchange activity for renal disease but P and C ANCA Vasculitis had risen to 14% followed by renal transplant at 10%, Goodpasture's Syndrome at 6% and transplant FSGS at 5% with Cryoglobulinemia 2% and Myeloma Nephropathy had dropped dramatically to less than 1% with no cases of SLE reported. This report describes the most common indications for plasma exchange in patient's with renal disease and the evidence that supports it's use in 2011.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.416
GPT teacher head0.476
Teacher spread0.059 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations25
Published2012
Admission routes2
Has abstractyes

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