Association between childhood allergic diseases, educational attainment and occupational status in later life: systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Childhood allergic diseases may prevent affected children from achieving their academic potential. Potential mechanisms include absence from school due to illness and medical appointments. Experience of symptoms in classes or leisure time, and stigma associated with visible signs and symptoms, including skin disease, requirements for medication during school time or the need for specific diets, may also contribute to reduced educational attainment. Studies have investigated the association between specific allergic diseases and educational attainment. The aim of this study is to systematically review the literature on allergic diseases, educational attainment and occupational status, and if possible, calculate meta-analytic summary estimates for the associations. METHODS: Systematic electronic searches in Medline, EMBASE, Cochrane, Cumulative Index to Nursing & Allied Health Literature (CINAHL), PsycINFO and education Resources Information Center (ERIC); hand search in reference lists of included papers and conference reports; search for unpublished studies in clinical trial registers and the New York Academy of Medicine Grey Literature Report; data extraction; and study quality assessment (Newcastle-Ottawa Scale) will be performed. ANALYSIS: Data will be summarised descriptively, and meta-analysis including meta-regression to explore sources of heterogeneities will be performed if possible. ETHICS AND DISSEMINATION: Dissemination in a peer-reviewed, open-access, international scientific journal is planned. PROSPERO REGISTRATION NUMBER: CRD42017058036.
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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 | 0.050 | 0.045 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.075 | 0.007 |
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".