Using postoperative SNOT-22 to help predict the probability of revision sinus surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it