Management of infectious disease syndromes in thoracic organ transplants and mechanical circulatory device recipients: a Delphi panel
Bibliographic record
Abstract
PURPOSE: Antimicrobial misuse contributes to antimicrobial resistance in thoracic transplant (TTx) and mechanical circulatory support (MCS) recipients. This study uses a modified Delphi method to define the expected appropriate antimicrobial prescribing for the common clinical scenarios encountered in TTx and MCS recipients. METHODS: An online questionnaire on managing 10 common infectious disease syndromes was submitted to a multidisciplinary Delphi panel of 25 experts from various disciplines. Consensus was predefined as 80% agreement for each question. Questions where consensus was not achieved were discussed during live virtual live sessions adapted by an independent process expert. RESULTS: An online survey of 62 questions related to 10 infectious disease syndromes was submitted to the Delphi panel. In the first round of the online questionnaire, consensus on antimicrobial management was reached by 6.5% (4/62). In Round 2 online live discussion, the remaining 58 questions were discussed among the Delphi Panel members using a virtual meeting platform. Consensus was reached among 62% (36/58) of questions. Agreement was not reached regarding the antimicrobial management of the following six clinical syndromes: (1) Burkholderia cepacia pneumonia (duration of therapy); (2) Mycobacterium abscessus (intra-operative antimicrobials); (3) invasive aspergillosis (treatment of culture-negative but positive BAL galactomannan) (duration of therapy); (4) respiratory syncytial virus (duration of antiviral therapy); (5) left ventricular assist device deep infection (initial empirical antimicrobial coverage) and (6) CMV (duration of secondary prophylaxis). CONCLUSION: This Delphi panel developed consensus-based recommendations for 10 infectious clinical syndromes seen in TTx and MCS recipients.
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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 | 0.079 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".