FungiScope<sup>™</sup>—Global Emerging Fungal Infection Registry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Rare invasive fungal diseases ( IFD ) are challenging for the treating physicians because of their unspecific clinical presentation, as well as the lack of standardised diagnostic and effective treatment strategies. Late onset of treatment and inappropriate medication is associated with high mortality, thus, urging the need for a better understanding of these diseases. The purpose of FungiScope ™ is to continuously collect clinical information and specimens to improve the knowledge on epidemiology and eventually improve patient management of these orphan diseases. FungiScope ™ was founded in 2003, and today, collaborators from 66 countries support the registry. So far, clinical data of 794 cases have been entered using a web‐based approach. Within the growing network of experts, new collaborations developed, leading to several publications of comprehensive analyses of patient subgroups identified from the registry. Data extracted from FungiScope ™ have also been used as the sole control group for the approval of a new antifungal drug. Due to the rarity of these diseases, a global registry is an appropriate method of pooling the scarce and scattered information. Joining efforts across medical specialities and geographical borders is key for researching rare IFD . Here, we describe the structure and management of the FungiScope ™ registry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it