Need for Alternative Trial Designs and Evaluation Strategies for Therapeutic Studies of Invasive Mycoses
Bibliographic record
Abstract
Studies of invasive fungal infections have been and remain difficult to implement. Randomized clinical trials of fungal infections are especially slow and expensive to perform because it is difficult to identify eligible patients in a timely fashion, to prove the presence of the fungal infection in an unequivocal fashion, and to evaluate outcome in a convincing fashion. Because of these challenges, licensing decisions for antifungal agents have to date depended heavily on historical control comparisons and secondary advantages of the new agent. Although the availability of newer and potentially more effective agents makes these approaches less desirable, the fundamental difficulties of trials of invasive fungal infections have not changed. Therefore, there is a need for alternative trial designs and evaluation strategies for therapeutic studies of invasive mycoses, and this article summarizes the possible strategies in this area.
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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 | 0.664 | 0.695 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".