Long‐term revision rates for endoscopic sinus surgery
Bibliographic record
Abstract
BACKGROUND: Reported revision rates for endoscopic sinus surgery (ESS) for chronic rhinosinusitis (CRS) vary significantly. Several investigations examining revision rates for ESS have been limited by duration of follow-up, academic centers, or small surgeon cohorts. The objective of this study was to define the long-term revision rates for ESS and to determine those unique patient factors that increase the risk of revision ESS. METHODS: The Utah Population Database was queried for Current Procedural Terminology codes for ESS from 1996 to 2016. Patient demographics and comorbid diagnoses were collected. Revision rates and risk factors for surgery were determined by Cox proportional hazard modeling. RESULTS: A total of 29,934 patients were identified, with a mean length of follow-up of 9.7 years. The long-term revision rate was found to be 15.9%. The mean time between surgeries decreased with higher number of revision surgeries. The time between the first and second surgery was 4.39 years and the time between the fourth and fifth surgery decreased to 2.18 years. Female gender, older age at first surgery, nasal polyps, comorbid asthma, allergy, and a family history of CRS all increased the risk of requiring revision surgery (p < 0.001). CONCLUSION: The long-term revision rate for ESS exceeds 15% and the time between revision surgeries decreased with each additional surgery being performed. Unique patient factors increased the risk of requiring revision ESS. Understanding patients' risk for revision surgery may help physicians select and counsel patients with CRS undergoing ESS.
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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 | 0.008 |
| 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 | 0.001 |
| 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".