Hyperferritinemia Screening to Aid Identification and Differentiation of Patients with Hyperinflammatory Disorders
Bibliographic record
Abstract
High ferritin is an important and sensitive biomarker for the various forms of hemophagocytic lymphohistiocytosis (HLH), a diverse and deadly group of cytokine storm syndromes. Early action to prevent immunopathology in HLH often includes empiric immunomodulation, which can complicate etiologic work-up and prevent collection of early/pre-treatment research samples. To address this, we instituted an alert system at UPMC Children's Hospital where serum ferritin > 1000 ng/mL triggered real-time chart review, assessment of whether the value reflected "inflammatory hyperferritnemia (IHF)", and biobanking of remnant samples from consenting IHF patients. We extracted relevant clinical data; periodically measured serum total IL-18, IL-18 binding protein (IL-18BP), and CXCL9; retrospectively classified patients by etiology into infectious, rheumatic, or immune dysregulation; and subjected a subgroup of samples to a 96-analyte biomarker screen. 180 patients were identified, 30.5% of which had IHF. Maximum ferritin levels were significantly higher in patients with IHF than with either hemoglobinopathy or transplant, and highly elevated total IL-18 levels were distinctive to patients with Stills Disease and/or Macrophage Activation Syndrome (MAS). Multi-analyte analysis showed elevation in proteins associated with cytotoxic lymphocytes in all IHF samples when compared to healthy controls and depression of proteins such as ANGPT1 and VEGFR2 in samples from hyperferritinemic sepsis patients relative to non-sepsis controls. This real-time IFH screen proved feasible and efficient, validated prior observations about the specificity of IL-18, enabled early sample collection from a complex population, suggested a unique vascular biomarker signature in hyperferritinemic sepsis, and expanded our understanding of IHF heterogeneity.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".