VITAMIN D AND HEALTH SERVICE UTILIZATION FOR UPPER RESPIRATORY TRACT INFECTIONS AND ASTHMA IN YOUNG CHILDREN
Bibliographic record
Abstract
Upper respiratory tract infections (URTI) and asthma are major causes of childhood morbidity. They are two of the most common reasons for children seeking health care in the outpatient and inpatient settings, posing a large burden on society and the health care system. It has been suggested that vitamin D may decrease airway inflammation and have an immune protecting effect, which may lead to a reduced risk of health service utilization (HSU) for URTI and asthma in childhood. If vitamin D has an impact on HSU for these common health issues, measures to increase vitamin D status could potentially reduce the burden of these diseases. This doctoral thesis uses observational methods to examine the relationship between 25-hydroxyvitamin D concentration in children ages 0-6 years, vitamin D supplementation during pregnancy and childhood and HSU over 2 years for URTI and asthma in childhood. Healthy children ages 0-6 years were recruited through the TARGet Kids! practice based research network from 7 primary care clinics in Toronto ON, Canada. Data from Ontario’s publicly funded health-care system was linked to TARGet Kids! data at the individual level and used to evaluate the relationship between vitamin D and HSU for URTI and asthma in childhood. I did not find statistically significant evidence to support an association between 25-hydroxyvitamin D concentration, vitamin D supplementation in pregnancy and childhood and HSU for URTI or asthma in a sample (n=2926) of healthy children ages 0-6 years that generally had adequate vitamin D concentrations. However, due to the relatively low frequency of asthma outcomes, future studies could be conducted in an asthma prevalent cohort with low 25-hydroxyvitamin D levels. Measuring asthma prevalence in young children is challenging, thus I conducted a measurement study to evaluate the agreement between the Ontario Asthma Surveillance Information System (OASIS) algorithm (a validated health services algorithm) and parent-reported asthma in children ages 1-5 years. The OASIS algorithm performed similarly to parent-reported asthma and may be a useful tool for identifying children 1-5 years of age to develop a cohort of children with a high likelihood of having asthma for inclusion into future studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".