Risk factors for severe infections in secondary immunodeficiency: a retrospective US administrative claims study in patients with hematological malignancies
Bibliographic record
Abstract
Real-world data are lacking to identify patients with secondary immunodeficiency (SID) who may benefit most from anti-infective interventions. This retrospective analysis used the IQVIA PharMetrics® Plus database to assess baseline characteristics associated with risk of severe infections post-SID diagnosis in patients with hematological malignancies. In 4066 patients included, the mean number of any and severe infections per patient in the one-year pre-SID diagnosis period was 9.5 and 0.7, respectively. Post-SID diagnosis, the mean annualized number of any and severe infections was 19.1 and 1.5, respectively. Receiver operating characteristic curve analysis identified a threshold (cutoff) of three bacterial infections at baseline as optimally predictive of severe infections post-SID diagnosis. Multivariate analysis indicated that hospitalizations, infections (≥3), or antibiotic use pre-SID diagnosis were predictive of severe infections post-SID diagnosis. Evaluation of these risk factors could inform clinical decisions regarding which patients may benefit from prophylactic anti-infective treatment, including immunoglobulin replacement if warranted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".