Sleep Dysfunction is an Independent Predictor of Productivity Losses in Patients with Chronic Rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is known to have a significant impact on economic productivity. Sleep dysfunction is associated with staggering productivity losses and is highly prevalent in patients with CRS. The effect of sleep dysfunction on productivity in CRS has not been elucidated. The objective of this study was to determine the relationship between sleep dysfunction and lost productivity in patients with CRS. METHODS: Eighty-two adult patients with CRS were prospectively enrolled into a cross-sectional cohort study. Patients with obstructive sleep apnea were excluded. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). Presenteeism (reduced work efficiency), absenteeism (missed work days), and lost work, household, and overall productivity were analyzed. The primary aim was assessing the correlation between PSQI and productivity. Regression analyses were performed to account for disease severity, pain, and depression. RESULTS: < .001). Higher PSQI scores were significantly associated with productivity losses, whereas lower scores were not. Sleep remained an independent predictor of productivity when regression analysis accounted for disease severity, depression, and pain. CONCLUSION: Sleep dysfunction has a significant association with lost productivity in patients with CRS, particularly with worsening PSQI scores. More clearly defining those components of CRS that most impact a patient's daily function will allow clinicians to more optimally manage and counsel patients with CRS.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".