Socioeconomic factors impact quality of life outcomes and olfactory measures in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Healthcare disparities related to socioeconomic factors may adversely impact disease states and treatment outcomes. Among patients with chronic rhinosinusitis (CRS), the impact of socioeconomic factors on outcomes following endoscopic sinus surgery (ESS) remains uncertain. METHODS: Adult patients with refractory CRS were prospectively enrolled into an observational, multi-institutional cohort study between March 2011 and June 2015. Socioeconomic factors analyzed included household income, insurance status, years of education completed, race, age, and ethnicity. Income was stratified according to the Thompson and Hickey model. The 22-item Sino-Nasal Outcome Test (SNOT-22) and Brief Smell Identification Test (BSIT) were completed preoperatively and postoperatively. RESULTS: A total of 392 patients met inclusion criteria. Higher age and male gender were associated with better mean preoperative SNOT-22 scores (both p < 0.02), whereas Medicare insurance status and male gender were associated with worse preoperative mean BSIT scores (both p < 0.02). Postoperatively, higher household income ($100,001+/year) and lower age were associated with a greater likelihood of improving at least 1 minimal clinically important difference (MCID) on SNOT-22 scores (OR = 2.40 and 1.03, respectively, both p < 0.05), while no factors were associated with increased odds of achieving a MCID on BSIT scores. CONCLUSIONS: Preoperative olfactory function and postoperative quality of life (QOL) improvement were associated with metrics of socioeconomic status in patients with CRS electing ESS. The odds of experiencing a clinically meaningful QOL improvement were more than twice as likely for patients with the highest household income level compared to other income tiers. Further investigation is warranted to identify barriers to postoperative improvement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".