[Clinical study of repairing donor site of thickness from cicatricial skin with auto-scalp grafting].
Bibliographic record
Abstract
OBJECTIVE: To study the effects of using auto-scalp for repairing donor site of thickness from cicatricial skin with auto-scalp grafting. METHODS: A total of 13 cases with donor site of thickness from cicatricial skin from January 2011 to December 2011 were analyzed. Wounds of donor site from cicatricial skin were grafted with auto-scalp and scalp were fixation was applied with negative pressure. The survival rate of auto-scalp graft was observed at Day 7 post-operation. At Month 12, hyperplastic scars at these donor sites of cicatricial skin were assessed through Vancouver Scar Assessment Table, scar itch assessment and scar proliferation rate. Wounds in the other thirteen cases with donor site of thickness from cicatricial skin from January 2010 to December 2010 were covered with vaseline gauze as control. RESULTS: No significant difference existed in the gender and age of the two groups patients (P > 0.05). The auto-scalp graft all survived. And the average healing time of donor-site wound in cicatricial skin in grafting group (7 days) was significantly decreased than that of control group (a mean of 20 days) (P < 0.01). After followed up for twelve months, the scar formation assessment value (1.5 ± 0.5), scar itch assessment (1.2 ± 0.4) and scar proliferation rate (14.6% ± 7.6%) in grafting group were significantly less than those of control group (6.7 ± 1.1, 2.0 ± 0.7, 55.8% ± 12.2%, all P < 0.01). CONCLUSION: Auto-scalp grafting may greatly shorten the healing procedure and ameliorate the quality of donor-site of thickness from cicatricial skin.
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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 | 0.000 | 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) | 0.002 | 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".