Complications of auricular cartilage harvest in rhinoplasty: Keloid and epidermal cyst formation
Bibliographic record
Abstract
Keloid scars and epidermoid cysts present unique challenges in plastic surgery, often requiring distinct diagnostic and therapeutic approaches. Keloid scars result from dysregulated wound healing characterized by collagen overproduction and inflammatory states. In contrast, epidermoid cysts are cutaneous lesions lined with keratinized epithelium, with the rare complication of development into squamous cell carcinoma. A rare clinical dilemma is when epidermoid cysts arise within keloidal scar tissue. In this case, effective management involves meticulous diagnostic approaches, including ultrasonography and histopathological examination, to identify coexisting cysts within scar tissue. In the few studies reporting this rare occurrence, various treatment protocols exist consisting of various combinations of surgical excision, intralesional corticosteroid injections, chemotherapeutic agents, laser therapy, radiotherapy, isotretinoin, and tranilast. As advancements in the comprehension and treatment of epidermoid cysts within keloid scars progress, customized therapeutic approaches provide promise for enhancing patient outcomes and quality of life.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".