Antifungal stewardship in the UK: where are we now?
Bibliographic record
Abstract
Background: Antifungal stewardship (AFS) is the judicious use of today's antifungal agents with the aim of improving patient outcomes and preserving their future effectiveness. Antifungal resistance (AFR) is increasing globally, with more patients at risk of Invasive Fungal Disease (IFD), highlighting the urgent need to standardize AFS practices in the UK. The aim of this position paper is to understand the current AFS landscape in the UK. Methods: A virtual panel discussion was held from September to October 2023 on an online platform followed by a virtual meeting with nine healthcare professionals from across the UK selected for their expertise on IFD management and AFS. The discussion was structured across four topics: current AFS landscape, key elements of an AFS programme, diagnostics and diagnostic stewardship, and unmet needs in education and training. A thematic analysis was carried out. The results represent the collated and summarized views from these activities. Results and discussion: Participants reported barriers to implementing AFS and its integration within antimicrobial stewardship (AMS) programmes in the UK. The primary challenge identified was a lack of resources, including funding and staff time. Sub-optimal fungal diagnostics and limited mycology expertise was reported as a barrier to AFS, clinical IFD and AFR surveillance. Approaches to combatting these challenges may include investing in formal mycology networks to serve as centres of clinical expertise and diagnostic hubs. Conclusion: National standards for AFS services and associated outcome metrics need to be established to set a benchmark for centres to improve AFS.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".