Prevention of Autologous Costal Cartilage Graft Warping in Secondary Rhinoplasty
Bibliographic record
Abstract
BACKGROUND: Autogenous costal cartilage grafts (ACCG) are frequently used in secondary rhinoplasty; however, these grafts tend to warp. The objective of this study is to systematically evaluate current interventions to prevent warping of ACCGs and to assess long-term outcomes with their use. METHODS: A systematic review was undertaken using a computerized search. Eligible articles assessed adult patients undergoing secondary rhinoplasty with ACCGs. Interventions to reduce warping were examined. Publication descriptors were extracted, heterogeneity was examined, and methodological quality of articles was assessed. RESULTS: Eighteen studies were included. Most studies were published after 2010 (83.3%), assessed a single intervention (83.3%), and were of levels of evidence III and IV. Mean patient age was 30 (range 5-95 years) and studies included a mean of 64 cases (range 9-357). Nine of the 15 non-comparative studies were considered of high methodological quality, while all 3 comparative studies were considered high quality. Secondary rhinoplasties which did not describe a method to address warping showed increased rates of warping compared to counter balancing techniques, chimeric grafts, titanium microplating, Kirschner wire and suture usage, irradiation, and various carving techniques. Rates of warping remained low with no major complications with the use of a variety of approaches. CONCLUSIONS: ACCG warping during secondary rhinoplasty can be alleviated with a variety of techniques with no clear difference in outcomes between approaches. Plastic surgeons may consider adopting one of the various techniques described in order to reduce warping, maximize aesthetic outcomes and patient satisfaction.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".