Impact of Septal Deviation on Recurrent Chronic Rhinosinusitis after Primary Surgery: A Matched Case‐Control Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of untreated deviated nasal septum (DNS) on recalcitrant chronic rhinosinusitis (CRS) among patients undergoing revision endoscopic sinus surgery (ESS). STUDY DESIGN: Case-control study. SETTING: Tertiary academic center. SUBJECTS AND METHODS: We performed a retrospective review of 489 patients undergoing revision ESS for CRS at a tertiary academic center. Patients undergoing septoplasty were matched to nonseptoplasty controls based on age and sex. Preoperative Lund-Mackay score (LMS) was compared between cohorts. Linear regression was used to identify predictors of LMS and ostiomeatal complex (OMC) obstruction. RESULTS: Thirty-six matched pairs (72 patients) were selected for analysis: 36 undergoing septoplasty and revision ESS and 36 undergoing revision ESS alone. Compared with nonseptoplasty controls, the septoplasty group had a significantly higher average LMS (17.8 vs 14.6, P = .02) and a greater rate of OMC obstruction (89% vs 61%, P < .01). The septoplasty group also had significantly higher opacification scores in the maxillary (1.5 vs 1.2, P = .03) and posterior ethmoid (1.8 vs 1.4, P = .02) sinuses. On multivariable analysis, DNS was an independent predictor of LMS ( P = .02) and OMC obstruction ( P < .01). CONCLUSION: Untreated DNS is associated with radiographic markers of CRS severity among patients undergoing revision ESS and may contribute to the multifactorial pathogenesis of persistent 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".