The impact of passive smoking on the development of lower respiratory tract infection in infancy Original Article
Bibliographic record
Abstract
Aim: The goal of this study was to evaluate the impact of passive smoking on the development of lower respiratory tract infection LRTI in children aged 0 24 months Material and Method: 95 infants with LRTI and 95 healthy control infants were included in the case control study conducted by random sample method Urinary cotinine creatinine ratios u CCR were determined in all children Smoking habits of their parents were evaluated Data were analysed by Chi square Mann Whitney U and Pearson correlation tests Results: The frequency of LRTI was increased in children with exposure to passive smoking The incidence of LRTI was also increased as the number of cigarettes smoked increased The infants whose mothers were active smokers had more LRTI compared to those whose mothers were non smokers OR= 2 5 p= 0 026 The prevalence of passive smoking was quite high in both group sof children according to u CCR 95 8 92 7 respectively The prevalence of passive smoking detected with quantitative measurements among children was higher than parental self reports Conclusions: Passive smoking prevalence was very high in infants with LRTI and in healthy infants However passive smoking exposure and smoking density among infants with LRTI were higher than healthy infants Turk Arch Ped 2009; 44: 12 7 Key words: Infant lower respiratory tract infection passive smoking
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".