The pain–depression dyad and the association with sleep dysfunction in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Depression, pain, and sleep disturbance is a symptom cluster often found in patients with chronic illness, exerting a large impact on quality of life (QOL). A wealth of literature exists demonstrating a significant association between depression, pain, and sleep dysfunction in other chronic diseases. This relationship has not been described in patients with chronic rhinosinusitis (CRS). METHODS: Sixty-eight adult patients with CRS were prospectively enrolled. Patients at risk for depression were identified using the Patient Health Questionnaire-2 (PHQ-2) using a cut-off score of ≥1. Pain experience was measured using the Brief Pain Inventory Short Form (BPI-SF) and the Short Form McGill Pain Questionnaire (SF-MPQ). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). RESULTS: Forty-seven patients were at risk for depression. Significant positive correlations were found between total PSQI scores and all pain measures (R = 0.38-0.61, p ≤ 0.05) and between total PSQI scores and PHQ-2 scores (R = 0.46, p < 0.05). For patients at risk for depression, significant, positive correlations were found between pain measures, the total PSQI score, and the 3 PSQI subdomains (sleep latency, sleep quality, and daytime dysfunction; R = 0.31-0.61, p < 0.05). The relationship between pain and sleep dysfunction scores was not seen in the absence of depression. CONCLUSION: Depression, pain, and sleep dysfunction are interrelated in patients with CRS. In the absence of depression, significant correlations between pain and sleep are not observed, suggesting that depression plays a key role in this interaction. Further research is needed to investigate the complex relationship between depression, pain, and sleep dysfunction in CRS.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".