Dyad of pain and depression in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Pain and depression often coexist as comorbidities in patients with chronic disease and exert a major impact on quality of life (QOL). Little is known about the relationship between pain and depression in chronic rhinosinusitis (CRS). Our objective was to investigate this relationship and to analyze the effect of pain and depression on QOL in CRS. METHODS: Patients with CRS were prospectively recruited as part of an observational cohort study. A total of 70 participants provided pain scores using both the Brief Pain Inventory Short Form (BPI-SF) and the Short Form McGill Pain Questionnaire (SF-MPQ). Patients at risk for depression were identified using the Patient Health Questionnaire-2 (PHQ-2). CRS-specific QOL was determined using the 22-item Sino-Nasal Outcome Test (SNOT-22). RESULTS: Significant positive correlations were found between depression scores and all pain measures (R = 0.475 to 0.644, p < 0.001). Patients with a PHQ-2 score ≥1 had significantly higher scores on all reported pain measures. Significant positive correlations were found between all pain measures, the total SNOT-22 score, and 3 SNOT-22 subdomains (sleep, psychological dysfunction, and ear/facial symptoms; R = 0.323 to 0.608, p < 0.05). CONCLUSION: Adult patients with CRS at risk for depression experience more pain and have overall worse disease-specific QOL. Further research investigating the complex interactions between depression and pain and the role it plays in CRS disease-specific QOL is warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".