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Record W4210310843 · doi:10.2196/36591

Bacterial Contamination of Intensive Care Units, Sana’a City, Yemen, 2019

2022· article· en· W4210310843 on OpenAlexvenueno aff
Mohammed Al Amad, Intesar Abdulsalam Al Shargabi, Samar Saeed Nasher, Sami Ahmed Al-Haidari, Riham Al-Dubaiee, Suaad Ameen Moghalles, Khaled Abdullah Al Moayad

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsnot available
Fundersnot available
KeywordsAcinetobacterMedicineBacillus cereusIntensive care unitStaphylococcus aureusMicrobiological cultureStaphylococcusContaminationMicrobiologyVeterinary medicineIntensive careCoagulaseBacteriaBiologyInternal medicineAntibioticsIntensive care medicine

Abstract

fetched live from OpenAlex

Background Bacterial contamination of the intensive care unit (ICU) is one of the risk factors responsible for the high incidence of nosocomial infections that can significantly increase the mortality among ICU patients. Objective The aim of this study was to investigate the bacterial contamination and pathogen isolates from the ICU environment of hospitals in Sana’a city, Yemen. Methods A descriptive cross-sectional study was performed in Sana’a city hospitals from December 5 to December 15, 2019. All hospitals that frequently reported mortality among ICU patients were included. A sterile swab moistened in sterile normal saline was used for sample collection. Seven ICU sites were targeted, including the patient’s bed, bedside table, masks of the oxygen-supplying apparatus, intravenous (IV) stand, door knob, wall, and floor, and two samples from each site were collected. The samples were transported to the National Center of Public Health Laboratory for microbiological culture. Results A total of 112 swabs were collected from the ICUs of eight hospitals. Among these, 87 (77.7%) yielded positive bacterial growth and 109 bacterial strains were isolated, including 62.4% (n=68) gram-positive and 37.6% (n=41) gram-negative bacteria. Coagulase-negative Staphylococcus, Staphylococcus aureus, and Bacillus cereus were the predominant gram-positive bacteria isolated, which accounted for 27.5% (n=30), 21.1% (n=23), and 10.1% (n=11) of all 109 bacterial isolates, respectively. Klebsiella species, Pseudomonas species, and Acinetobacter were the main gram-negative isolates obtained, accounting for 12.8% (n=14), 12.8% (n=14), and 11.9% (n=13) of all 109 bacterial isolates, respectively. The common contaminated sites were the patients’ beds/bedside tables (40/109 strains, 36.7%), floors (24/109 strains, 22.0%), walls (15/109 strains, 13.8%), and masks of the oxygen-supplying apparatus (12/109 strains, 11.0%). The door knobs and IV stands were contaminated by 9 strains, representing 8.3% of the total isolated bacteria. Conclusions The contamination of ICU environments was high and patients’ surroundings were the most contaminated areas. Implementations of strict quality standards of hygienic practices and effective cleaning of inanimate surfaces by the hospitals’ infection control units along with periodic monitoring by the health authority are highly recommended.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.027
GPT teacher head0.290
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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