Epidemiology and control of antibiotic resistance in the intensive care unit
Bibliographic record
Abstract
PURPOSE OF REVIEW: Resistance to antibiotics is very high in the intensive care units of many countries, although there are several exceptions. Some infections are becoming extremely difficult to treat. The risk of cross-transmission of those strains is very high. This review focuses on recent data (2003 to the present) that may help understanding and dealing with this serious public health problem. RECENT FINDINGS: Intensive care units can be considered as 'factories' for creating, disseminating and amplifying resistance to antibiotics, for many reasons: importation of resistant microorganisms at admission, selection of resistant strains with an extensive use of broad-spectrum antibiotics, cross-transmission of resistant strains via the hands or the environment. Some national programs can be considered as failures, as in the UK and the USA. Other countries have been able to maintain a low level of resistance (Scandinavian countries, Netherlands, Switzerland, Germany, Canada). There is clearly an 'inoculum effect' above which preventive measures become poorly efficient. Several preventive measures have been proposed including preventive isolation, systematic screening at admission, local, national or international antibiotic guidelines, antibiotic prescriptions advice by infectious-disease teams, antibiotic prevention with selective digestive decontamination, antibiotic strategies such as 'cycling', or rather, for some authors, the use of an 'à la carte' antibiotic strategy which could be considered as a 'patient-to-patient antibiotic rotation'. SUMMARY: There is obviously an international concern regarding the level of resistance to antibiotics in the intensive-care-unit setting. A strong program including prevention of cross-transmission and better usage of antibiotics seems to be needed in order to be successful. We do not know if this kind of program will enable countries with a very high endemic level of resistance to decrease the level in future years.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".