Hypertrophic Scar, Wound Contraction and Hyper-Hypopigmentation
Bibliographic record
Abstract
For decades, hypertrophic scarring, contraction, and pigment abnormalities have altered the future for children and adults after thermal injury. The hard, raised, red and itchy scars; shrunken wounds; and hyper- and hypo-pigmented scars are devastating to physical and psychosocial outcomes. The specific causes remain essentially unknown and, at present, prevention and treatment are symptomatic and marginal at best. Hypertrophic scarring is the major significant negative outcome after survival from of a thermal injury. Hypertrophic scars are hard, raised, red, itchy, tender, and contracted.1,2 These scars are ugly, disfiguring, and uncomfortable and may diminish, but never totally go away. Hypertrophic scarring after deep partial-thickness wounds is common. We have reviewed the English literature on the prevalence of hypertrophic scarring3 and found that children, young adults, and people with darker, more pigmented skin are particularly vulnerable and, in this subpopulation, the prevalence is up to 75%.4,–6 Hypertrophic scarring is devastating and can result in disfigurement and scarring that affects quality of life which, in turn, can lead to lowered self esteem, social isolation, prejudicial societal reactions, and job discrimination.7,–12 Scarring also has profound rehabilitation consequences, including loss of function, impairment, disability, and difficulties pursuing recreational and vocational pursuits.10,13,14
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".