Low-dose immune tolerance induction therapy in severe hemophilia a children in China: Starting earlier resulted in better inhibitor eradication outcomes
Bibliographic record
Abstract
BACKGROUND: Shorter interval-time from inhibitor detection to starting immune tolerance induction (ITI) might predict better ITI outcomes for severe Hemophilia A (SHA) patients with high-risk-inhibitors. However, the prediction-impact of interval-time for these patients on low-dose ITI strategy remained unclear. OBJECTIVES: To explore the relationship between interval-time and low-dose ITI outcomes in Chinese SHA children with high-risk-inhibitors. METHODS: This was a single-center, retrospective study on SHA children with high-risk-inhibitors (each with immediate pre-ITI inhibitor titer>10 Bethesda Units/mL) undergoing low-dose ITI strategy for ≥24 months. ITI outcomes and their predictive factors were evaluated at the 24th month treatment for each patient. The predictive ability of interval-time on ITI success was determined using receiver operating characteristic (ROC) curve. RESULTS: Among 47 patients investigated, 34 (72.3 %) achieved success. Independent predictor for ITI-outcome on multivariate analysis included the interval-time (p = 0.007) and peak inhibitor-titer (p = 0.011). Shorter interval-time predicted ITI success [cut-off value = 22.3 months, area under ROC-curve (AUC) = 0.701] and early-ITI success within 12 month (cut-off value = 9.4 months AUC = 0.704). Linear regression analysis suggested each month interval-time delay delayed success by 0.1552 month. Unlike the interval-time, peak inhibitor-titer had no success-predictive value in high-peak inhibitor-titer patients on ITI with immunosuppressants. CONCLUSIONS: Interval-time represented a strong predictive value for outcomes in our low-dose ITI strategy for SHA patients with high-risk-inhibitors. Shorter interval-time was associated with higher success rate and earlier success achievement. The respective interval-time cut-off values were 22.3 months for ITI success and 9.4 months for early-success.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".