In the face and neck, keloid scar distribution is related to skin thickness and stiffness changes associated with movement
Bibliographic record
Abstract
Keloid scars tend to occur in high-tension sites due to mechanical stimuli that are involved in their development. To date, a detailed analysis of keloid distribution focused specifically on facial and neck areas has not been reported, and limited literature exists as to the related mechanical factors. To rectify this deficiency of knowledge, we first quantified the facial and neck keloid distribution observed clinically in 113 patients. Subsequently, we performed a rigorous investigation into the mechanical factors and their associated changes at these anatomic sites in healthy volunteers without a history of pathologic scarring. The association between keloid-predilection sites and sebaceous gland-dense and acne-prone sites was also examined. To assess skin stretch, thickness and stiffness, VECTRA, ultrasound and indentometer were utilised. Baseline skin stiffness and thickness were measured, as well as the magnitude of change in these values associated with facial expression and postural changes. Within the face and neck, keloids were most common near the mandibular angle (41.3%) and lateral submental (20.0%) regions. These areas of increased keloid incidence were not associated with areas more dense in sebaceous glands, nor linked consistently with acne-susceptible regions. Binomial logistic regression revealed that changes in skin stiffness and thickness related to postural changes significantly predicted keloid distribution. Skin stiffness and thickness changes related to prolonged mechanical forces (postural changes) are most pronounced at sites of high keloid predilection. This finding further elucidates the means by which skin stretch and tension are related to keloid development. As a more detailed analysis of mechanical forces on facial and neck skin, this study evaluates the nuances of multiple skin-mechanical properties, and their changes in a three-dimensional framework. Such factors may be critical to better understanding keloid progression and development in the face and neck.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".