The clinical study of the relationship between the defect of different thickness dermal tissues and the proliferative scar formation
Bibliographic record
Abstract
Objective To explore the relationship between the defect of different thickness dermal tissues and the scar formation after burn injury. Methods The involved 32 donor sites of burn patients were divided into two groups: split thickness skin (Group A) and intermediate split thickness skin (Group B) from donor site. Meanwhile, according to condition whether autograft was necessary, Group A was divided into the following sub-groups respectively: group A1 (without autograft) and group A2 (split thickness autograft) and Group B into group B1 (without autograft), group B2 (split thickness autograft) and group B3 (intermediate split thickness autograft). The skin grafts either from donor site or autograft were collected for measurement of the dermal thickness by computer. The extent of the hypertrophic scars in donor site was also evaluated by Vancouver scoring system six months postoperatively. Results The dermal defect was 0.146-0.163 mm in Group A but 0.456-0.656 mm in Group B. The value of Vancouver scores became higher with increase of dermal defect but reduced when the lost dermis was regrafted to the wound. Conclusions It is proportional between dermal defect and scar formation. The scar proliferation would relieve if the dermis is regrafted to the wound.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".