Using preoperative <scp>SNOT</scp>‐22 score to inform patient decision for Endoscopic sinus surgery
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The purpose of this study is to improve patient understanding of surgical outcomes while they make a preference-sensitive decision regarding electing endoscopic sinus surgery (ESS) for chronic rhinosinusitis (CRS). STUDY DESIGN: Prospective observational cohort study. METHODS: Patients with CRS who elected ESS were prospectively enrolled into a multi-institutional, observational cohort study. Patients' were categorized into 10 preoperative Sino-Nasal Outcome Test (SNOT-22) groups based on 10-point increments beginning with a score of 10 and ending at 110. The proportion of patients achieving a SNOT-22 minimal clinically important difference (MCID) (9 points) and the percentage of relative improvement (%) for each preoperative SNOT-22 group were calculated. A subgroup analysis based on polyp status was performed. RESULTS: A total of 327 patients were included in this study. Patients with a SNOT-22 score between 10 and 19 had the lowest chance of achieving an MCID (37.5%) and received a relative mean worsening of their quality of life (QoL) after ESS (+18.8%). Patients with a SNOT-22 score greater than 30 obtained a greater than 75% chance of achieving an MCID, and there was a relative improvement of 45% in QoL (all < -44.9%) after ESS. Outcomes from the polyp status subgroup analysis were similar to the findings from the overall cohort. CONCLUSION: Outcomes from this study suggest that patients with a preoperative SNOT-22 score higher than 30 points receive a greater than 75% chance of achieving an MCID and on average obtain a 45% relative improvement in their QoL after ESS. Patients with SNOT-22 score of less than 20 did not experience improved QoL from 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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".