Cumulative evidence for association of rhinitis and depression
Bibliographic record
Abstract
BACKGROUND: Several primary studies evaluated the association between rhinitis and the incidence of depression and yielded inconsistent results. We conducted a meta-analysis of studies evaluating the association between rhinitis and depression. METHODS: We searched the EMBASE, PubMed and Cochrane Library databases for studies published in English before April 1, 2019. The studies were included if they reported any type of rhinitis in relation to depression. Two authors independently extracted the data. The odds ratios (ORs) were pooled using a random-effects model. Stratified analyses were conducted to evaluate the association. RESULTS: Among the 3472 initially identified studies, we included 14 studies involving a total of 19.36 ± 1.1 million participants according to predefined inclusion criteria. The associations between rhinitis (R), allergic rhinitis (AR), and nonallergic rhinitis (NAR) and depression were significant with ORs of 1.86 (95% CI 1.32 to 2.62, p < 0.05), 1.54 (95% CI 1.24 to 1.90, p < 0.05), and 2.15 (95% CI 1.49 to 3.09, p < 0.05), respectively. The results were consistent and statistically significant in all subgroup analyses. CONCLUSIONS: Rhinitis was associated with an increased risk of depression. Further prospective studies involving large sample sizes are required to confirm the results by considering more confounders and clarify the mechanisms.
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".