Determining a Successful Nasal Airway Surgery: Calculation of the Patient‐Centered Minimum Important Difference
Bibliographic record
Abstract
Objective Determine whether the patient-identified minimum important difference (MID) in Nasal Obstruction Symptom Evaluation (NOSE) score differs from a statistically calculated estimate of MID in patients with septal deviation undergoing nasal airway surgery. Study Design Prospective cohort. Setting Tertiary academic referral center. Subjects Patients with nasal obstruction due to septal deviation. Methods Patients completed the NOSE questionnaire preoperatively and indicated the change from their baseline score that they would consider the minimum improvement required to define the septoplasty with turbinate reduction as successful. A previously published distribution-based approach was used to estimate the MID based on baseline NOSE scores. Scores were reported both as a raw score and as a percentage of patients' baseline scores. One-sample t test was used to compare the statistically estimated MID to the patient-reported MID. Results Seventy-six patients were included. The mean (SD) baseline NOSE score was 12.9 (4.03). The mean (SD) patient-identified MID was 5.3 (2.1), corresponding to a 41.1% change (95% confidence interval, 37.2-41.3) from baseline. The statistically estimated MID was 5.2 points (40.3% reduction from baseline scores). The estimated MID was not significantly different from the patient-identified MID ( P = .4). Conclusion In patients with septal deviation, an improvement of approximately 40% in their nasal obstructive symptoms as assessed by the NOSE questionnaire is required to define a nasal airway surgery as successful. The patient-identified and the statistically calculated MIDs were similar. Furthermore, this MID can be used to guide research, improving the ability to use the NOSE score as a dichotomous scoring measure (treatment success/failure) and estimating sample size.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".