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SEVERE HEMATOLOGICAL MANIFESTATIONS IN SLE: 2 CASES OF THROMBOTIC THROMBOCYTOPENIC PURPURA

2025· article· en· W4410513237 on OpenAlexvenueno aff
Joanna Gil, Roxana González Mazarío, Pablo Martínez Calabuig, Laura Salvador Maicas, Mireia Lucía Sanmartín Martínez, Iván Jesús Lorente Betanzos, Cristina Campos Fernández

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineThrombotic thrombocytopenic purpuraPurpura (gastropod)Thrombocytopenic purpuraImmunologyImmunopathologyPlateletSchistocyte

Abstract

fetched live from OpenAlex

PV209 / #633 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Thrombotic thrombocytopenic purpura (TTP) is a rare but life-threatening hematological complication of systemic lupus erythematosus (SLE). In our cohort of 229 SLE patients, TTP was observed in 0.87% of cases. This report presents the clinical course and management of these patients. Methods We reviewed the medical records of 2 patients diagnosed with TTP secondary to SLE. Both cases were characterized by microangiopathic hemolytic anemia, thrombocytopenia, and multiorgan involvement. Treatment protocols, outcomes, and therapeutic responses were analyzed. Results • Case 1: Treated with intravenous methylprednisolone boluses (IVMP), intravenous immunoglobulin (IVIG), rituximab, and 12 cycles of eculizumab. The patient achieved remission with resolution of hematological and organ involvement. • Case 2: Treated with IVMP boluses, IVIG, and rituximab, leading to normalization of blood parameters and recovery of organ function. Conclusions TTP in SLE was identified in 0.87% of our cohort. Prompt recognition and aggressive treatment with IVMP, IVIG, and rituximab proved effective, with eculizumab playing a crucial role in refractory cases. Multidisciplinary collaboration and individualized treatment strategies are essential to improve outcomes in this rare but severe complication.

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)0.001
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 score0.196
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.296
Teacher spread0.272 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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