Fourteen Year Surveillance of Nosocomial Infections in Neurology Unit
Bibliographic record
Abstract
Background: The purpose of this study was to evaluate the long-term data of Neurology Unit and emphasize the importance of hospital infection control. Methods: This study was performed between January 1997 and December 2010. The surveillance method was active, prospective, and based on laboratory and patient. Active surveillance of nosocomial infections (NIs) was performed by infection control team, using the criteria proposed by the CDC (The Centers for Disease Control and Prevention) and National Nosocomial Infections Surveillance System (NNIS) methodology. Results: During the study period, 435 episodes were detected in 384 patients. The overall incidence rates (NI/100) and incidence densities (NI/1,000 days of stay) of NIs were 3.7% (range 1.0 - 7.7) and 3.2/1,000 patient-day (range 0.8 - 7.2/1,000), respectively. The most common nosocomial infection by primary site was urinary tract infections (32%), and pneumonia (25.1%). The most prevalent microorganisms were coagulase-negative staphylococci (39.4%), Escherichia coli (18%), Staphylococcus aureus (10%) and Klebsiella spp. (9.9%). Conclusion: We conclude that development of nosocomial infection will be prevented by monitoring the patients in fully-equipped intensive care units, the rapid termination of invasive procedures, appropriate antibiotic therapy and discharging the patient, significantly. doi:10.4021/jnr113w
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".