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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".