Patient Complaints With Primary Versus Revision Rhinoplasty: Analysis and Practice Implications
Bibliographic record
Abstract
BACKGROUND: Rhinoplasty patients often present with specific concerns and are frequently exacting in their demands and expectations of the surgical experience. OBJECTIVES: The authors assess the presenting complaints expressed during the rhinoplasty consultation process and compare the presentations of primary versus revision rhinoplasty patients. METHODS: A retrospective review of 400 consecutive rhinoplasty patients was performed. Demographic information and patient concerns regarding nasal appearance and function were recorded. Complaint frequencies (as well as rank order) were compared between primary and revision patients. Statistically significant associations were compared in more detail through logistic regression models. RESULTS: Primary rhinoplasty patients were significantly more likely to cite "too large" and "dorsal hump" as motivating concerns. Conversely, revision rhinoplasty patients were far more likely to cite concern regarding a "crooked nose," "tip asymmetry," "wide or large nostrils," "dorsal sloop," and "columellar show." Revision rhinoplasty patients also complained of issues such as "alar retraction," "pointy tip," and "nasal scarring," which were almost negligible in frequency in the primary rhinoplasty group. CONCLUSIONS: Patients presenting for primary rhinoplasty commonly seek a smaller, more refined nasal appearance. Patients with prior rhinoplasty operations are far more likely to raise concern regarding crookedness or asymmetries. By comparing the presentations of primary and revision rhinoplasty patients-and delineating the common indications for revision operations-novice rhinoplasty surgeons may be able to avoid certain pitfalls at the outset, thereby reducing their revision rates. The data may also assist surgeons in developing a more targeted approach to the consultation process in the revision setting.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".