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Record W1987909555 · doi:10.1586/eci.09.58

Primary and secondary hemophagocytic lymphohistiocytosis: clinical features, pathogenesis and therapy

2009· review· en· W1987909555 on OpenAlexaff
Sumit Gupta, Sheila Weitzman

Bibliographic record

VenueExpert Review of Clinical Immunology · 2009
Typereview
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsHemophagocytic lymphohistiocytosisPerforinMedicineImmunologyHemophagocytosisMacrophage activation syndromeGranzymeHepatosplenomegalyMalignancyDiseasePathologyImmune systemPancytopeniaBone marrow

Abstract

fetched live from OpenAlex

Hemophagocytic lymphohistiocytosis (HLH) is a potentially fatal hyperinflammatory syndrome with prolonged high fever, hepatosplenomegaly and characteristic laboratory findings. HLH may be inherited (primary) or may be secondary to any severe infection, malignancy or rheumatologic condition. The last several years have witnessed an explosion in our understanding of HLH. Of the inherited causes for which the underlying genetic cause is known, most involve abnormalities of proteins important in the exocytosis cytolytic pathway, whereby perforin and granzymes are delivered to a target cell to induce apoptosis. The exact mechanisms underlying this process remain unclear. However, when a known genetic defect is not present, the diagnosis of HLH is still made on a constellation of clinical features and good clinical judgment. Rapid diagnosis is crucial, as early therapy with immunosuppressive agents and/or proapoptotic chemotherapy can be life-saving. This article examines recent advances in our understanding of the pathophysiology, clinical features, diagnosis, etiology and treatment of HLH, as well as the challenges that lie ahead.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.089
GPT teacher head0.468
Teacher spread0.379 · 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 designTheoretical or conceptual
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

Citations182
Published2009
Admission routes1
Has abstractyes

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