Meeting the Therapeutic Challenges of Emergent and Rare Invasive Fungal Diseases Through Novel Clinical Trial Designs
Bibliographic record
Abstract
Treatments for emerging and rare invasive fungal diseases (IFDs) represent a critical unmet medical need. For IFDs that occur less frequently than invasive aspergillosis, such as mucormycosis, hyalohyphomycosis, and phaeohyphomycosis, randomized controlled clinical trials are impractical and unlikely to meet urgent public health needs. Understanding regulatory approaches for approval of drugs for rare cancers and rare metabolic diseases could help meet the challenges of studying drugs for rare IFDs. A single-arm, controlled clinical trial with a high-quality external control(s), with confirmatory evidence from nonclinical studies, including pharmacokinetic/pharmacodynamic data in predictive animal models of the disease may support findings of effectiveness of new drugs and biologics. Control populations may include historical controls from published literature, patient registries, and/or contemporaneous external control groups. Continuous engagement among clinicians, industrial sponsors, and regulatory agencies to develop consensus on trial design and innovative development pathways for emergent and rare invasive fungal diseases is important.
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 | 0.278 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".