Socio-demographic factors related to under-diagnosis of childhood asthma in Upper Silesia, Poland
Bibliographic record
Abstract
Introduction.The presented study of 4,535 children aged 7-17 years in the Upper Silesian region of Poland yielded 186 cases of previously known asthma, and 44 children with newly diagnosed asthma.The aim of the presented study was to identify non-medical factors that could explain why children with a newly established diagnosis ('undiagnosed asthma') had not been diagnosed in the past. Materials and method.The study was performed according to a case-control design.Parents of the children answered questionnaires on socio-economic status and family-related factors.Statistical determinants of undiagnosed asthma were explored using raw (OR) and logistic odds ratios with their 95% confidence intervals (logOR, 95%CI). Results.Children with undiagnosed asthma were younger compared to the group with previously known asthma (11.3±2.1 vs. 12.6±2.5 years; p=0.0008).Newly diagnosed cases were more frequent in children who had less parental attention (less than 1 hour/day spent by parent with child -OR=4.36;95%CI: 1.76-10.81)and who were not registered with specialized health care (OR=2.20;95%CI: 0.95-5.06).Results of logistic regression analysis suggest that under-diagnosis of asthma is related to age below 12 years -logOR = 3.59 (95%CI: 1.28-10.36),distance to a health centre > 5 km -logOR = 3.45 (95%CI: 1.05-11.36),time spent with child < 1 hour/day -logOR = 6.28 (95%CI: 1.98-19.91).Conclusion.Among non-medical determinants of undiagnosed asthma the age of a child plays a major role.Another factors of importance is the large distance between residence and health centre, and low parental attention at home.
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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 | 0.001 | 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".