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Record W2781936946 · doi:10.1177/2054358117747262

Case Series of 3 Patients Diagnosed With Atypical Hemolytic Uremic Syndrome Successfully Treated With Steroids, Plasmapheresis, and Rituximab

2017· article· en· W2781936946 on OpenAlexaff
Jeffery M. Patterson, Lauren Bolster, Loree Larratt

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineThrombotic microangiopathyMicroangiopathic hemolytic anemiaRituximabAtypical hemolytic uremic syndromeThrombotic thrombocytopenic purpuraPlasmapheresisEculizumabADAMTS13Kidney diseaseInternal medicineDermatologyGastroenterologyPediatricsDiseaseImmunologyPlateletAntibodyComplement system

Abstract

fetched live from OpenAlex

RATIONALE: Atypical hemolytic uremic syndrome, which has a high probability of chronic kidney disease, morbidity, and mortality, needs to be promptly recognized when patients present with microangiopathic hemolysis. PRESENTING CONCERNS OF THE PATIENT: Three patients present with laboratory parameters consistent with a thrombotic microangiopathy. With a suspected diagnosis of thrombotic thrombocytopenic purpura, steroids with plasmapheresis were initiated. DIAGNOSES: With ADAMTS13 levels reported normal, the suspected diagnoses were reevaluated. Given ongoing renal impairment, atypical hemolytic uremic syndrome was strongly considered. INTERVENTIONS: When local funding issues precluded the prompt use of eculizumab, 4 doses of weekly rituximab were trialed. OUTCOME: Over 2 years later, all 3 patients have sustained durable remissions defined by the absence of kidney impairment or laboratory investigations concerning for microangiopathic hemolytic relapse. LESSONS LEARNED: In cases of a suspected autoimmune mechanism leading to atypical hemolytic uremic syndrome, long-term use of eculizumab may not be required.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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