The price of pain in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is associated with productivity losses exceeding US$13 billion annually. Although pain is well known to significantly affect patient productivity in other diseases, its economic impact on CRS-related lost productivity has not been examined. The objective of this study was to determine whether CRS-related facial pain correlates with lost productivity in patients with CRS. METHODS: Seventy patients with CRS were enrolled in a cross-sectional investigation. Patients with a history of systemic inflammatory disease, ciliary dysfunction, chronic pain syndromes, migraines, and fibromyalgia were excluded. Pain was measured using the Brief Pain Inventory Short Form (BPI-SF) and the Short-Form McGill Pain Questionnaire (SF-MPQ). Presenteeism, absenteeism and lost work, and household and overall productivity were assessed. Regression analysis was performed to assess potential confounders, including depression. RESULTS: Pain as measured with BPI-SF and SF-MPQ total scores correlated with all domains of productivity losses. Overall, lost productivity was significantly correlated with pain (R range, 0.354-0.485; p < 0.001). Presenteeism (reduced work efficiency) had the highest correlation with all of the overall pain scores (R range, -0.366 to -0.515; p < 0.001). Lost household productivity time was the least affected by pain (R range, 0.267-0.389; p < 0.05). These correlations remained statistically significant after regression analysis, which accounted for depression (p < 0.05). CONCLUSION: A significant correlation exists between CRS-related facial pain and productivity losses in patients with CRS that is independent of depression. Facial pain has the strongest correlation with presenteeism, which is the main driver of productivity losses and indirect costs associated 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".