Research on the proliferative scars treated by skin regenerative method treatment
Bibliographic record
Abstract
Objective: To observe the efficacy of skin regenerative medical technique in treating proliferative scars. Method: Select 32 patients (age16-52) with proliferative scars after burns or wound for 1-11 years,which include 25(male) and 7(female). 2 scar similar spots are chosen in each patient for self-comparison.After the experimental group uses the scar detachment, scar Pi Huizhi applies the beautiful valuable moist burn medicinal plaster gauze cover the cooperation of Chinese and Western medicine home position skin regenerative method treatment; After the control group uses the scar detachment, scar Pi Huizhi applies the petroleum jelly cover the traditional method treatment.The observation comparison curative effect, applies the Vancouver scar appraisal meter appraisal scar proliferation situation. Results: Two groups return to the scar skin which plants to survive.The experimental group regenerates the skin to be good, the cicatrization speed and the quality surpass the control group (P 0.05); The scar detachment latter 9 months, the experimental group Vancouver scar appraised the meter appraised surpasses the control group (P0.05) front, the scar result ache, sao itches and so on symptom vanishing, the skin piece smooth also the color returns plants improves obviously, is close with the periphery skin. Conclusion: To from the body skin source insufficiency, the scar area big proliferative scar patient or is not willing to increase newly for the skin area injured area patient, the home position skin regenerates the cooperation of Chinese and western medicine method treatment using scar detachment scar Pi Huizhi is one ideal method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.003 | 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".