Calm in the midst of cytokine storm: a collaborative approach to the diagnosis and treatment of hemophagocytic lymphohistiocytosis and macrophage activation syndrome
Bibliographic record
Abstract
BACKGROUND: Hemophagocytic lymphohistiocytosis (HLH) and macrophage activation syndrome (MAS) were historically thought to be distinct entities, often managed in isolation. In fact, these conditions are closely related. A collaborative approach, which incorporates expertise from subspecialties that previously treated HLH/MAS independently, is needed. We leveraged quality improvement (QI) techniques in the form of an Evidence-Based Guideline (EBG) to build consensus across disciplines on the diagnosis and treatment of HLH/MAS. METHODS: A multidisciplinary work group was convened that met monthly to develop the HLH/MAS EBG. Literature review and expert opinion were used to develop a management strategy for HLH/MAS. The EBG was implemented, and quality metrics were selected to monitor outcomes. RESULTS: An HLH/MAS clinical team was formed with representatives from subspecialties involved in the care of patients with HLH/MAS. Broad entry criteria for the HLH/MAS EBG were established and included fever and ferritin ≥500 ng/mL. The rheumatology team was identified as the "gate-keeper," charged with overseeing the diagnostic evaluation recommended in the EBG. First-line medications were recommended based on the acuity of illness and risk of concurrent infection. Quality metrics to be tracked prospectively based on time to initiation of treatment and clinical response were selected. CONCLUSION: HLH/MAS are increasingly considered to be a spectrum of related conditions, and joint management across subspecialties could improve patient outcomes. Our experience in creating a multidisciplinary approach to HLH/MAS management can serve as a model for care at other institutions.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".