Características clínicas y microbiológicas de infecciones por Klebsiella pneumoniae productora de carbapenemasa MBL, tipo NDM, Hospital Geriátrico San Isidro Labrador EsSalud, 2018
Bibliographic record
Abstract
Objective: To determine the clinical and microbiological characteristics of seven (7) cases of New Delhi MBL (NDM)producing Klebsiella pneumoniae nosocomial infections at the Hospital Geriátrico San Isidro Labrador (HG SIL) from February to May 2018. Materials and methods: A descriptive and retrospective study conducted in elderly people with urinary infections, pneumonia, sacral ulcers and surgical wound infection who received multiple non-protocolized antibiotic therapy. The methods used to detect MBLs in the Klebsiella pneumoniae isolates that showed decreased sensitivity to carbapenems were the phenylboronic acid-based (PBA) and ethylenediaminetetraacetic acid-based (EDTA) double disk tests. The identification of the NDM gene was performed by polymerase chain reaction at the Instituto Nacional de Salud. Results: In total, the seven cases were positive for the NDM gene and resistant to meropenem, which confirmed the epidemic outbreak. Mortality accounted for 28.6 % (2 of 7 patients) of the cases. However, due to the presence of comorbidities in all cases, the attributable mortality could not be determined. Conclusions: Labs have a key initial role for detecting and classifying the carbapenemases and the measures of comprehensive control of infections. The aforementioned cases are the first ones reported in our healthcare network.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".