Verbesserungsbedarf in der Versorgung von Patienten/-innen mit Clostridioides-difficile-lnfektionen (CDI) – Experten/-innenmeinung im internationalen Vergleich
Bibliographic record
Abstract
INTRODUCTION: infection (CDI), as a nosocomial disease, is associated with high morbidity and mortality. Even though the incidence of CDI has been declining in Germany in recent years, the individual infection may pose a medical challenge despite therapeutic advances. The aim here is to clarify which gaps practitioners consider to be particularly serious in care and in the existing evidence base. METHODS: In a moderated workshop of German CDI experts the topics considered as relevant were identified. A survey already conducted in five other countries (Australia, France, Great Britain, Canada, and Italy) was adapted and processed by 27 practitioners. During the evaluation, the topics perceived as particularly important were identified, the statements of the specialist groups were compared and changes in opinion were considered. RESULTS: 27 fully completed questionnaires were evaluated. The need for improvement was primarily seen in the prevention of CDI recurrences (74.1%) and the treatment of recurrences (55.6%). Evidence deficits were noted in the treatment of recurrences (55.6%) and identification of risk factors for recurrences (48.1%). Improving care via fecal microbiota transfer (FMT) was named by 70.4%. For guidelines, more clarity (48.1%) and more regular updates (40.7%) were desired. For patients, better education on appropriate antibiotic use (52.0%) and choice of FMT were desired (48.1%). SUMMARY: The German expert view and the international assessment is similar, when asked about the need for improvement in care and evidence gaps in the treatment of patients with CDI: The focus is on prevention and therapy of recurrent CDI. The problem of access to FMT is a German peculiarity that seems to need improvement.
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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 | 0.045 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".