Association of the Frequency of Respiratory Illness in Early Childhood with a Change in the Distribution of Blood Lymphocyte Subpopulations
Bibliographic record
Abstract
Little is known about the distribution of lymphocyte phenotypes in young children and the association specific phenotypes may have with respiratory illnesses. The objective of this study was to describe lymphocyte distributions in children at approximately 2 years of age and to test for associations with the frequency of respiratory illness during the first 2 years of life. We hypothesized that an increased frequency of illness would be associated with those phenotypes that reflect previous antigen exposure and/or immune activation. Seventy-three children were followed during their first 2 years of life with daily symptom diaries and twice-monthly telephone calls to ascertain the incidence of respiratory illness. After the children reached 2 years of age, the phenotypes of circulating blood lymphocytes were measured by flow cytometry. Associations between illness and phenotypes were adjusted for education level of parents; hours per week in day care; hours per week exposed to environmental tobacco smoke, mould, or water damage in bedroom; and parental history of allergy and asthma. The resulting median lymphocyte count was 4.0 x 109 per litre (standard deviation, 1.3) with a CD4/CD8 count of 2.28, consistent with published values. Illness rates were positively associated with the percentage of CD8+ CD38+ T cells (unadjusted p = .03, adjusted p = .014), CD8+ CD45RO+ T cells (unadjusted p = .06, adjusted p = .036), and CD4+ CD45RO+ T cells (unadjusted p = .01, adjusted p = .005). Our conclusions is that there is an association between the distribution of lymphocyte phenotypes and the incidence of respiratory illness early in life. Future research is recommended to determine the directionality of this association.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".