Antibiothérapie et antibioprophylaxie de l'endocardite infectieuse – Une prise de position SPILF-AEPEI sur les recommandations 2023 de l'ESC
Bibliographic record
Abstract
• L'AEPEI et la SPILF proposent des recommandations sur le traitement antibiotique curatif et préventif de l'EI sur la base des recommandations de l'ESC publiées en 2023. • L'antibioprophylaxie chez les patients à haut risque d'EI ne doit être prescrite qu'avant une situation bucco-dentaire à risque. • Dans l'EI streptococcique, le choix de la β-lactamine doit être basé sur la CMI. • Dans l'EI prothétique staphylococcique, le traitement est une bithérapie associant β-lactamine ou daptomycine à la gentamicine puis la rifampicine après stérilisation des hémocultures. • Le relai oral de l'antibiothérapie orale est possible chez certains patients bien sélectionnés atteints d'EI streptococcique. • AEPEI and SPILF provide a consensus statement on the antibiotic treatment and prophylaxis of IE based on the ESC guidelines published in 2023. • Antibiotic prophylaxis in patients at high risk of IE should be prescribed only before oro-dental situations at risk. • In streptococcal IE, the choice of β-lactam should be based on the MIC. • In staphylococcal prosthetic valve IE, two antibiotics should be used, β-lactam or daptomycin with gentamicin replaced by rifampicin after sterilization of blood cultures. • Switch to oral antibiotic therapy may be used in selected patients with streptococcal IE.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".