Prevalence of Bacterial Lower Respiratory Tract Infections at a Tertiary Hospital in Jordan
Bibliographic record
Abstract
Background: Lower respiratory tract infections (LRTI) are a major cause of morbidity and mortality globally. The World Health Organization (WHO) estimates that LRTI are the most common global cause of death from infectious diseases. However, the specific etiologic agent associated with LRTI is often unknown. Aims: We determined the bacterial infections and seasonal patterns associated with LRTI among hospitalized cases at Jordan University Hospital (JUH) for a period of five years. Methods: We conducted a multi-year study among hospitalized patients in Jordan on LRTI-associated bacterial etiology. Results: We found bacterial infections among 105 (21.1%) out of 495 LRTI patients. The most frequently identified bacteria in the LRTI patients were Staphylococcus aureus (7.7%) followed by Pseudomonas aeruginosa (5.1%). Most of the LRTI patients (95.2%) had at least one chronic disease and many were admitted to the Intensive Care Unit (16.8%). Of the 18 (3.64%) patients with LRTI who died at the hospital, 2 had a bacterial infection. We noticed a seasonal pattern of bacterial infections, with the highest prevalence during the winter months. Conclusions: Our findings suggest that early identification of bacterial agents and control of chronic disease may improve clinical management and reduce morbidity and mortality from LRTI.
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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 | 0.001 | 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) | 0.003 | 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".