Bee product efficacy in children with upper respiratory tract infections
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The most common infectious disease in children is acute upper respiratory tract infection (URTI). Many drugs, especially antitussive drugs, are used for symptomatic treatment. Bee products (propolis, royal jelly, and honey) have antiviral, antibacterial, and antioxidant properties, and they have synergistic effects with antibiotics. The aim of this study was to evaluate the effectiveness of a mixture of bee products in URTI in children. METHODS: The patients were divided into four groups consisting of two bacterial groups receiving either antibiotics or antibiotics + bee products and two viral groups treated with either placebo or bee products. Disease severity and improvement duration were assessed by the Canadian Acute Respiratory Illness and Flu Scale (CARIFS) Score. RESULTS: One hundred and four patients (59 male, 56.7%; 45 female, 43.3%) aged between 5‒12 years were included in the study. Fifty patients (48%) were evaluated for bacterial infections and 54 (52%) for viral infections. Patients with viral infection receiving a mixture product showed earlier improvement, compared to placebo group. CARIFS scores were significantly lower in antibiotic + mixture group on day-2 and day-4, compared to antibiotic alone group (p < 0.05). None of the patients developed any reactions or side effects to the mixture product. CONCLUSIONS: Bee products are effective in symptomatic treatment of upper respiratory tract infections. Bee products can be considered as a good treatment option because the available drugs already used for symptomatic treatment are not cost effective and can also have serious side effects in children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".