A Network for Advancing Prevention and Treatment of Infections Among Immunocompromised Individuals
Bibliographic record
Abstract
Importance: Immunocompromised individuals are a large and growing population who are at increased risk for infectious diseases. There has and continues to be a lack of focus on clinical trials to establish the safety and efficacy of therapies for infectious diseases in immunocompromised patients. The establishment of a US-based clinical trial network to improve the study and subsequent implementation of therapies and strategies to treat and prevent infections in immunocompromised individuals would address this gap in research infrastructure and jumpstart public and private investment. Observations: A national interdisciplinary meeting was convened on September 10, 2024, in Bethesda, Maryland, to discuss the outsized impact of infectious diseases in immunocompromised individuals and to identify the primary gaps and opportunities for clinical trials in this population. Approaches to achieve this goal include obtaining dedicated funding and support through public-private partnerships to establish alignment and feasibility for high-priority areas of research. This article outlines the relevance of this work; ongoing efforts to collaborate with the National Institutes of Health, US Congress, industry, and philanthropy to obtain funding for mutually beneficial outcomes; the network structure; and perspectives from clinicians, regulatory agencies, the pharmaceutical industry, and patients. Conclusions and Relevance: There is a dearth of evidence to support the use of many therapies for infectious diseases in immunocompromised individuals, which has substantial impact at the individual and societal level. A multipronged approach to improve integration of, and funding for, rigorous research in this population into the core priorities of the public and private sectors could address important public health gaps by developing evidence-based guidance to protect a vulnerable community.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.087 | 0.039 |
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".