Validation of the <scp>HScore</scp> and the <scp>HLH</scp>‐2004 diagnostic criteria for the diagnosis of hemophagocytic lymphohistiocytosis in a multicenter cohort
Bibliographic record
Abstract
Timely diagnosis of hemophagocytic lymphohistiocytosis (HLH) is critical and relies on clinical judgment. The HLH-2004 criteria are commonly used diagnostic criteria, whereas HScore was recently developed for reactive HLH. OBJECTIVE: In this external validation study, we sought to compare the diagnostic accuracy of the HLH-2004 criteria and HScore and identify optimal cutoffs stratified by underlying etiology. METHODS: In this retrospective cohort of all hospitalized adults in Alberta, Canada, (1999-2019) who had ferritin >500 ng/ml and underwent either biopsies or soluble CD25 testing, we calculated the diagnostic accuracy of HLH-2004 and HScore for the overall population and different etiologies. RESULTS: Of 916 patients, 98 (11%) had HLH. HLH-2004 criteria ≥5 predicted HLH with a sensitivity of 91%, specificity of 93%, positive predictive value of 90%, and negative predictive value of 94% (c-statistic 92%). HScore ≥169 predicted HLH with better sensitivity (96%) but reduced specificity (71%), whereas the optimal cutoff ≥200 performed comparably to HLH-2004. HLH-2004 criteria outperformed HScore in most etiologies, whereas HScore improved sensitivity in inflammatory/autoimmune-HLH. The optimal cutoff of HScore was higher in hematopoietic cell transplant due to higher prevalence of fevers and cytopenias. CONCLUSION: HLH-2004 criteria and HScore demonstrated excellent discriminatory power in identifying HLH. HScore may improve diagnostic accuracy in autoimmune-HLH.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".