Establishment of a French surveillance system of Clostridiodes difficile infection: Comparison of patient’s characteristics with other national and European data
Bibliographic record
Abstract
INTRODUCTION: The magnitude and scope of Clostridioides difficile infection (CDI) has changed with an increase in incidence and severity. The epidemiology of CDI is not well known in France due to difficulties to conduct large continuous surveillance. The objectives were to compare the characteristics of patients with CDI collected through repeated point prevalence survey via DIFTEC™, a free electronic tool, with those from previous French or European studies. METHODS: DIFTEC™ was developed to evaluate epidemiological burden, diagnostic strategies and management of CDI in France. National and European guidelines were used for definitions. A literature review of studies conducted in Western Europe on CDI and published between January 2008 and May 2018 was done to compare their data with those included in the DIFTEC™ database. RESULTS: From January 2016, to December 2017, 455 CDI episodes from 22 French hospitals were included. Most of CDI cases were health-care associated (HCA) (78%). The comparison between included patients and French literature data showed that the rates of previous antibiotics exposure, crude mortality and recurrence were not statistically different. However HCA-CDI was significantly more frequent in the DIFTEC™ study. Gender distribution, recurrence and crude mortality rates were not statistically different compared to European data. HCA-CDI was more frequent in the DIFTEC™ study whereas previous treatment with proton pump inhibitors and antibiotics were significantly higher in European studies. DISCUSSION: These results illustrated the added value of a new tool for increasing the reliable knowledge of CDI in France based on epidemiological surveillance implemented in health-care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".