Validation of the <scp>HScore</scp> and the <scp>HLH</scp>‐2004 diagnostic criteria for the diagnosis of hemophagocytic lymphohistiocytosis in a multicenter cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it