Ideal Surgical Incision Lines Minimizing Tension: A Proposal Based on Observations of Hypertrophic Scars and Keloids
Bibliographic record
Abstract
Background: Hypertrophic scars and keloids can develop from surgical incisions. These pathologic scars grow progressively due to chronic dermal inflammation, which is exacerbated by skin tension. Postsurgical wound tension can be mitigated by various surgical tactics. One underrecognized aspect is surgical incision orientation. Here, we discuss the principles underlying existing surgical incision line systems, including Langer and Pinkus lines, and propose a new system based on our observations of pathological scar growth. Methods: We review the biological mechanisms that drive pathological scar growth, describe existing surgical incision line systems and their limitations, and propose our "ideal surgical incision line" system. Results: Keloids grow in the direction of predominant skin-stretching tension. The main surgical incision line systems are those of Borges (relaxed skin tension lines caused by anatomical features), Kraissl (dynamic lines driven by muscle contraction), and Lemperle (striae distensae). Borges lines only consist of static lines; Kraissl lines are generally not accurate for dynamic wrinkles caused by multidirectional or multilayered muscle contraction; and not every patient bears striae distensae. Our experience with pathologic scars led us to develop an "ideal surgical incision" system for the face and entire body that incorporates static wrinkles, dynamic wrinkles caused by simple and multidirectional muscle contraction, and classical folds/creases/grooves. We also propose that when ideal lines cannot be used for practical reasons, Z-incisions and Z-plasties can mitigate skin tension. Conclusions: Our ideal surgical incision line system may help guide surgeons in their choice of surgical incision lines, thus reducing the risk of pathological scarring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".