Perforin and CD107a testing is superior to NK cell function testing for screening patients for genetic HLH
Bibliographic record
Abstract
, encoding proteins involved in cytotoxic lymphocyte degranulation. Natural killer (NK)-cell cytotoxicity assays can quickly screen for all of these genetic diseases, facilitating treatment, but combining NK-cell perforin expression and CD107a upregulation tests can as well. To determine the relative diagnostic accuracies for each approach, we retrospectively reviewed screening test performance in 1614 patients referred for HLH evaluation. For each test, we generated a receiver operating characteristic (ROC) curve, and calculated area under the curve (AUC) and diagnostic parameters at optimal threshold. We generated an AUC for combining perforin and CD107a tests by creating a logistic regression model and applying model-generated coefficients to patient values. Sensitivities of NK-cell function, perforin mean channel fluorescence (MCF), and CD107a MCF to detect biallelic mutations were 59.5%, 96.6%, and 93.8%, with specificities of 72.0%, 99.5%, and 73%. AUCs for NK-cell cytotoxicity, perforin MCF, CD107a MCF, and combined perforin and CD107a MCFs were 0.690, 0.971, 0.860, and 0.838. Perforin and CD107a tests are more sensitive and no less specific compared with NK cytotoxicity testing for screening for genetic HLH and should be considered for addition to current HLH criteria.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".