Cannula versus needle in medical rhinoplasty: the nose knows
Bibliographic record
Abstract
The use of hyaluronic acid (HA) fillers has become a popular alternative for nose remodeling, although poor understanding of the nasal anatomy has resulted in adverse events and generated some controversy. Among them, is the question of where and when to use cannulas vs needles. Through multiple cadaver dissections, clinical experience and the review of medical literature the authors conclude the use of needle over cannula is preferred, assuring a correct injection plane lying fully against the bone or cartilage. Although blunt in nature, cannulas may lead to difficulty in determining with precision the exact depth of product placement and contribute to more dissection of adjacent structures. Thorough knowledge of the highly variable nasal anatomy, including vessel depth location is of outmost importance in avoiding adverse events. Good patient selection is critical where most noses for augmentation rhinoplasty and some reduction rhinoplasty candidates where the goal is to camouflage the dorsal hump are amenable to medical rhinoplasty, unless there is reduced skin elasticity of nasal soft tissues or distortion of anatomy from surgery or trauma. Appropriate product selection is important for effective results. The authors suggest fillers with low cohesivity and high lifting capacity. Finally, we suggest a technique referred as Rhinosculpting base in the use the use of three conceptual elements: facial analysis, light reflection, and use of HA gel as a cartilage graft, in combination with the detailed injection technique presented in this article, which ensures a safer and satisfying treatment outcome.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".