Multicenter validation of secondary hemophagocytic lymphohistiocytosis diagnostic criteria
Bibliographic record
Abstract
BACKGROUND: Five fulfilled hemophagocytic lymphohistiocytosis (HLH)-2004 criteria, and the HScore are widely used and recommended by international expert consensus to diagnose secondary HLH. Both diagnostic scores have never been validated in heterogeneous patient cohorts of secondary HLH patients. We aimed to systematically optimize and validate diagnostic criteria of secondary HLH using a multicenter approach. METHODS: We developed optimized criteria in our cohort of critically ill patients as a first step. We next validated these new criteria together with the original and modified HLH-2004 criteria as well as the HScore using original data of 13 published cohorts, which were identified by a systematic literature search. RESULTS: The best performing HLH diagnostic criteria sets over all 13 validation cohorts were the original HLH-2004 criteria with a decreased cut-off (cut-off 4, mean sensitivity 86.5%, mean specificity 86.1%), followed by the revised HLH-2004 criteria (natural killer cell activity removed; cut-off 4, mean sensitivity 83.8%, mean specificity 87.8%) and the HScore (cut-off 169, mean sensitivity 82.4%, mean specificity 87.6%). Our newly developed HLH diagnostic criteria showed inferior performance. Ferritin ≥500 µg/L had 94.0% mean sensitivity over all cohorts. CONCLUSIONS: In this first multicenter validation study, four fulfilled HLH-2004 criteria and an HScore of 169 were suitable to diagnose secondary HLH, which will lead to rapid diagnosis and improved patient outcomes. Ferritin proved as a reliable HLH screening marker. Our results should be taken into account in clinical recommendations and in designing new studies.
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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 | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".