Carbapenemase Producing Klebsiella species from Hospitalised Patients at Three Referral Hospitals in Yaounde; Cameroon
Bibliographic record
Abstract
Background: Carbapenemases are the most potent beta-lactamases capable of hydrolysing all beta-lactam drugs. Data scarcity on laboratory evidence of carbapenemase producers obstructs infection prevention and effective control measures. This study aimed to detect and phenotypically characterize Klebsiella species resistant to carbapenems. Method: A total of eighty-seven (87) Klebsiella spp. isolates were collected from three Yaounde-based tertiary hospitals over a period of twelve months (February 2019 to January 2020). The isolates were identified on API 20E and antimicrobial susceptibility testing was done according to the Kirby Bauer disc diffusion method. Carbapenem resistance was detected based on reduction in inhibition zone diameter of meropenem less than 28mm and resistance phenotypes determined by the combination disc synergy test. Results: Four Klebsiella species were identified namely: Klebsiella pneumoniae 62/87 (71.3%), K. oxytoca 11/87 (12.6%), K. ozaenae 7/87 (8%), K. rhinoscleromatis 7/87 (8%). Klebsiella pneumoniae was most resistant to carbapenems with a considerably high number of multidrug resistant isolates 39/56 (69.6%). Patients from the 0-9 years age group were most represented 29/87 (33.3%). Carbapenemases belonging to Ambler classes A, B and D were detected as follows: KPC 2/87 (2.3%), MBL 2/87(2.3%), OXA-48 4/87 (4.6%). All four Klebsiella species were producers of KPC carbapenemase 12/87 (13.8%) and Extended Spectrum β-lactamase 68/87 (78.2%) enzymes. Conclusion: These results revealed that more than half of Klebsiella species analysed were multidrug resistant and about a quarter in hospitalized patients at the referral hospitals were mostly KPC carbapenemase producers. This raises concerns over the need to control and confine the spread of these isolates of clinical and epidemiological importance for effective infection prevention and control.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".