Socioeconomic status impacts postoperative productivity loss and health utility changes in refractory chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Social determinants of health can have a substantial impact on treatment outcomes. Prior study has shown that socioeconomic status influences the likelihood of improvement in quality-of-life (QOL) following endoscopic sinus surgery (ESS). However, the impact of socioeconomic factors on changes in productivity loss and health utility after ESS remains unknown. METHODS: Adult patients (≥18 years of age) with chronic rhinosinusitis (CRS) who underwent ESS were prospectively enrolled into a multi-institutional cohort study. Productivity losses were calculated using the human capital approach and monetized using U.S. government-estimated wage rates. Health utility values (HUVs) were derived from the Medical Outcomes Study Short-Form-12 survey using University of Sheffield algorithms. Independent socioeconomic factors of interest included: age, gender, ethnicity, insurance status, educational attainment, and household income categorized via the Thompson-Hickey model. RESULTS: A total of 229 patients met inclusion criteria, and 163 (71%) provided postoperative follow-up. All subjects reported significant, within-subject improvement in both mean monetized productivity loss (p < 0.001) and HUV postoperatively (p < 0.001). Using paired sample statistics, patients with lowest income (≤$25,000/year) and with Medicare insurance did not report significant improvement in productivity loss (p ≥ 0.112) or HUV (p ≥ 0.081), although sample size limitations may have contributed to this finding. Patients in higher income tiers ($25,001 to $100,000/year and $100,001+/year) and those with employer-provided/private health insurance reported significant postoperative improvements in productivity loss and HUV (all p ≤ 0.003). CONCLUSION: Socioeconomic factors, including income and insurance provision, may impact improvements in productivity loss and HUV following ESS. Further research to validate these findings, ascertain mechanisms behind these results, and improve these outcomes 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".