Community-Acquired Methicillin-Resistant Staphylococcus aureus in Hospitals: Age-Specificity and Potential Zoonotic–Zooanthroponotic Transmission Dynamics
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) lineages are a devastating clinical and public health issue. Data on local lineage profiles are limited. We report on the frequency of community-acquired and hospital-acquired cases (CA-MRSA, HA-MRSA). We studied 147 isolates from King Khalid tertiary care hospitals (KKH), each from a case in a patient and including 33 patients at the Maternity and Children’s Hospital (MCH). Of the 147 isolates, 87 males (59%) and 60 females (41%) were in KKH. The overwhelming majority (80%; n = 119/147) were CA-MRSA in KKH. Intriguingly, despite significant differences between males (70%) and females (53%), lineage-acquisition remained age-specific around 58–60 years in both genders. However, while CA-MRSA dominated early in life (0–20, 70% MCH), it increased with age in KKH adults; 21–50 (28%), >50 (59%) until the overall 80% (n = 144/180). Major specimens included skin-wounds, surgeries (70.3%), blood (13.5%), sputum (8.8%), very rarely urine (4.1%), and nasal (3.4%), albeit most patients showed severe enteritis and necrotizing pneumonia. Antibiograms showed high beta lactam resistances, including amoxicillin–clavulanate (83%), oxacillin (84%), cefoxitin FOX (100%), penicillin and ampicillin (~100%), as well as high resistance (82%) to carbapenem. Fortunately, high susceptibility was seen to non-beta lactams and, to a lesser extent, gentamicin, erythromycin, and fusidic acid; 33%, 34%, and 38%, respectively, in KKH. A similar pattern was seen in MCH except for a low resistance pattern to gentamicin CN, clindamycin CD, erythromycin E, and tobramycin TOB; 34%, 31%, 39%, and 41%, respectively, except for fusidic acid. These findings have significant clinical implications for MRSA patient management strategies. Clinical- and lineage-profiles imply host-selection and zoonotic–zooanthroponotic transmission dynamics. Future molecular typing, sequencing, and characterization of dominant clone(s) is imperative.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".