Differences in infection prophylaxis measures between paediatric acute myeloid leukaemia study groups within the international Berlin–Frankfürt–Münster (I‐<scp>BFM</scp>) study group
Bibliographic record
Abstract
Prevention of infections is of obvious relevance in paediatric patients with acute myeloid leukaemia (AML). However, recommendations are often non-specific and supported by low-quality evidence, resulting in divergent infection preventive regimens. Using a web-based survey, we investigated the infection prophylaxis guidelines of 22 paediatric AML study groups affiliated to the international Berlin-Frankfürt-Münster study group. In order to evaluate differences in daily practice among hospitals, representatives (n = 27) from the Nordic Society for Paediatric Haematology and Oncology-Dutch-Belgium-Hong Kong - AML study group participated in a slightly modified survey. Seven study groups (32%) advise gram-negative antibiotic prophylaxis, mainly with fluoroquinolones (n = 6). Gram-positive prophylaxis is prescribed by eight groups (36%). Over 60% of the study groups prescribe food and social restrictions, but the specific topics and strictness differ widely. According to the hospital-based survey, sites roughly comply with common study group guidelines. However, the use of any gram-negative antibiotic prophylaxis, the specific prophylactic antifungal agent and the strictness of the food and social restrictions differ substantially between the hospitals. Despite a long history of close collaboration, many differences are still present between the affiliated groups. The results of this survey provide an appropriate baseline measure to study the emergence and impact of future guidelines on infection prophylaxis in paediatric AML.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".