Predicting the Results of Rhinoplasty Before Surgery: Easy Noses Versus Difficult Noses
Bibliographic record
Abstract
A major problem for many rhinoplastic surgeons is the ability to predict, before surgery, the difficulty of the procedure (whether the rhinoplasties will be technically easy or technically difficult to perform) and the success rate of the result (whether the rhinoplasty will likely give good results or poor ones).The present paper outlines a systematic approach to nasal analysis, allowing the surgeon to consistently estimate, before surgery, the degree of technical difficulty of each rhinoplasty, as well as predicting its future result in terms of patient satisfaction. This preoperative evaluation is based on the analysis of the skin texture and the osteocartilagenous framework on lateral and frontal views. It allows for the nose to be classified as green (easy), yellow (moderate) or red (difficult), depending on two factors: the degree of surgical difficulty and the expected patient's satisfaction with the result.The essence of the present paper is to introduce a simple, systematic approach to assist the novice rhinoplastic surgeon to assess the complexity, the risks and the expected outcome of a rhinoplasty in the preoperative period, rather than postoperatively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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) | 0.001 | 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".