Pneumococcal infection among hospitalized Egyptian children
Bibliographic record
Abstract
AIM: We aimed to describe the detection rate spectrum of clinical manifestations, and outcome of pneumococcal disease in children younger than 5 years admitted to the largest referral pediatric hospital in Egypt. MATERIALS AND METHODS: This was a hospital-based study to detect laboratory-confirmed Streptococcus pneumoniae cases among children younger than 5 years. Data on demographic characteristics, clinical diagnosis, comorbidities, diagnostic tests, antibiotic resistance, and clinical outcome were collected during the study years from 2008 to 2011. RESULTS: During the 4-year study period, 22 018 cases younger than 5 years had cultures performed at Cairo University Pediatric Hospital microbiology laboratory. We estimated the annual detection rate of total Streptococcus pneumonia infection to be 54.5/100 000. The incidence of invasive pneumococcal disease (IPD) was half the incidence of non-IPD (18.2 and 36.4/100 000, respectively). Infants of 1 year or younger were statistically more vulnerable to Streptococcus pneumonia infection compared with children between 1 and 5 years of age (annual rate: 110.5/100 000 and 21.6/100 000, respectively). The overall pneumococcal annual case fatality was 33.3% and was higher in IPD (75%) than in non-IPD (12.5%) cases. There was an obviously increasing trend of the pneumococcal detection rate throughout the 4 years of the study (P<0.0001). CONCLUSION AND RECOMMENDATIONS: Our results confirm the substantial and increasing pneumococcal infection, the emerging of multidrug resistant isolates, and the vulnerability of the younger age group and high-risk population, which calls for a national surveillance to inform policy and decision-making before national wide vaccine introduction.
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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".