EANM/SNMMI guideline/procedure standard for [18F]FDG hybrid PET use in infection and inflammation in adults v2.0
Bibliographic record
Abstract
Abstract Introduction Hybrid [ 18 F]FDG PET imaging is currently the method of choice for a wide variety of infectious and inflammatory disorders and was recently adopted in several clinical guidelines. A large amount of evidence-based articles, guidelines and appropriate use criteria have been published since the first version of this guideline in 2013. Purpose To provide updated evidence-based information to assist physicians in recommending, performing and interpreting hybrid [ 18 F]FDG PET examinations for infectious and inflammatory disorders in the adult population. Methods A systematic literature search of evidence-based articles using whole-body [ 18 F]FDG hybrid imaging on the indications covered within this guideline was performed. All systematic reviews and meta-analyses published within the last 10 years until January 2023 were identified in PubMed/Medline or Cochrane. For each indication covered in this manuscript, diagnostic performance was provided based on meta-analyses or systematic reviews. If not available, results from prospective or retrospective studies were considered based on predefined selection criteria. Results and conclusions Hybrid [ 18 F]FDG PET is extremely useful in the work-up and management of adults with infectious and inflammatory diseases, as supported by extensive and rapidly growing evidence-based literature and adoption in clinical guidelines. Practical recommendations are provided describing evidence-based indications as well as interpretation criteria and pitfalls. Monitoring treatment response is the most challenging but insufficiently studied potential application in infection and inflammation imaging.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".