Using postoperative SNOT-22 to help predict the probability of revision sinus surgery
Bibliographic record
Abstract
BACKGROUND: There is a need to develop a patient-level strategy to identify those at higher risk of requiring revision ESS since this may assist clinicians in tailoring their postoperative management. This study evaluated whether identifying changes in the post- operative 22-item Sinonasal Outcome Test (SNOT-22) can help identify patients at increased risk of needing revision sinus surgery for refractory chronic rhinosinusitis (CRS). METHODS: 668 CRS patients undergoing primary ESS with complete 60-month follow-up were evaluated in this prospective, longitudinal cohort study. Outcomes were evaluated in an unselected cohort and a low-risk cohort, which was comprised of patients without a history of asthma or aspirin sensitivity. RESULTS: Failing to achieve an improvement of greater than one minimal clinically important difference (MCID; 9 points) at 3 months after primary ESS and a deterioration of greater than one MCID (ie. >9 points) from the 3- to 12-month follow-up periods was associated with an increased risk of revision ESS in both the unselected and low-risk CRS cohorts. CONCLUSION: Outcomes from this study suggest that identifying MCID changes in the SNOT-22 score within 12 months after primary ESS can identify patients at increased risk for needing revision surgery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".