Reporting Outcomes and Outcome Measures in Open Rhinoplasty: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Comparative studies have shown little statistical difference in outcomes following rhinoplasty, demonstrating near equivalent results across all surgical techniques. Cross-study comparisons of these trials are difficult because variation in outcome reporting prevents statistical pooling and analysis. OBJECTIVES: The authors sought to identify all outcomes and outcome measures used to evaluate postoperative results in rhinoplasty. METHODS: An extensive computerized database search of MEDLINE and EMBASE was performed; all trials involving n ≥ 20 patients, aged 18 years and older undergoing a primary, open rhinoplasty procedure, were included for review. RESULTS: Of the 3235 citations initially screened, 72 studies met the stated inclusion criteria. A total of 53 unique outcomes and 55 postoperative outcome measures were identified. Outcomes were divided into 6 unique domains: objective signs, subjective symptom severity, physical function related to activities of daily living, patient satisfaction, surgeon satisfaction, and quality of life. The identified outcome measures consisted of 5 nasal-specific, author-reported instruments; 5 nasal specific, patient-reported instruments; 5 patient-reported, generic instruments; and 40 author-generated instruments. Of the outcome measures identified, the Rhinoplasty Outcomes Evaluation, Sino-Nasal Outcome Test-22, and FACE-Q were the only instruments to demonstrate adequate validity, reliability, and responsiveness to change in patients who underwent a rhinoplasty procedure. CONCLUSIONS: There is heterogeneity in the outcomes and outcome measures employed to assess postsurgical outcomes following rhinoplasty. A standardized core outcome set is urgently needed to make it possible for future investigators to compare results of various techniques in rhinoplasty 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 | 0.053 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".