How do questionnaire definitions of atopy status affect sample size calculations for asthma cohort studies in a population of Canadian children?
Bibliographic record
Abstract
Skin prick tests (SPT) are the gold standard for determining atopy. In epidemiological studies of childhood allergy, questionnaire responses are often used to define atopy and predict sample size. Questionnaire-reported hayfever symptoms have shown 28-76% sensitivity and 21-94% specificity compared to SPT. We evaluated how questionnaire definitions of atopy affect sensitivity, specificity and sample size calculations in a population of Canadian children. We used questionnaire data from 5619 Toronto schoolchildren participating in the 2006 T-CHEQ study to determine 3 possible questionnaire definitions of atopy, including having any 1, any 2 or all 3 parent-reported physician diagnoses of hayfever, eczema or food allergy. In a nested case-control sample of 208 of these children, atopy was evaluated by SPT to14 common aeroallergens. Using SPT as the gold standard for atopy, we calculated sensitivity, specificity and sample size for a nested cohort study of particulate exposure and atopy outcome. Compared with SPT, sensitivity, specificity and Youden’s index were 54.3%, 65.8% and 20.1% for 1 reported atopic condition and 24.4%, 98.7% and 23.1% for 2 reported atopic conditions, respectively (Table 1 ). Requiring at least 2 positive SPT for atopy did not change the sensitivity or specificity. Sample size calculations required 344 and 2948 participants for atopy defined by 1 or 2 atopic conditions, respectively. Questionnaire definitions of atopy in Canadian children have moderate sensitivity and specificity. More specific definitions decrease sensitivity and increase sample size requirement. Depending on the purpose of the proposed study, either definition of atopy may lead to an adequately-powered study.
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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 | 0.302 | 0.506 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".