Correlation between extent of sinus surgery, radiographic disease, and postoperative outcomes
Bibliographic record
Abstract
BACKGROUND: The extent of endoscopic sinus surgery (ESS) required for optimal outcomes in chronic rhinosinusitis (CRS) is undefined. We evaluated whether concordance between the extent of surgery and degree of radiographic disease influences postoperative outcomes. METHODS: 247 CRS patients who underwent ESS were retrospectively assigned a concordance score reflecting the similarity between the extent of surgery and degree of radiographic disease. 0 points were assigned when sinusotomy was performed on a diseased sinus, or no sinusotomy was performed on a nondiseased sinus; plus 1 for sinusotomy on a nondiseased sinus; and -1 for a diseased sinus left unopened. The total possible score ranged from minus 10 to plus 10. Patients were divided into 5 subgroups according to variance from complete concordance. SNOT-22 scores and revision rates were compared at 6 and 24 months. RESULTS: All five subgroups had similar preoperative SNOT-22 scores and improved at 6 months postoperatively. At 6 months postoperatively, the most conservatively operated and most extensively operated subgroups each achieved equivalent improvements in SNOT-22 as the completely concordant subgroup. At 24 months, the most extensively operated subgroup had a 12.5-point smaller improvement in SNOT-22 scores compared to the completely concordant subgroup. Multivariate analysis showed no association between concordance score and revision rate. CONCLUSIONS: Symptom improvement and revision rates after ESS do not appear to correlate with the degree of concordance between extent of surgery and radiographic disease. More extensive surgery than indicated by CT confers neither greater symptomatic improvement nor long-term detriment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".