Therapeutic effect of 90Sr dynamic therapy in the treatment of pathological scar
Bibliographic record
Abstract
Objective To study the therapeutic effect of surgery combined with 90Sr dynamic therapy used in pathological scar. Methods 323 cases of pathological scar were treated with 90Sr dynamic therapy after surgery from June 2010 to June 2014. Initial treatment regimen was made according to the growth characteristics of pathological scar. Then adjusting the treatment programs according to the treatment response.The treatment effect and complications were compared between the new treatment regimen and traditional regimen used in 252 patients (June 2006 to May 2010). SPSS 17.0 was used for statistical analysis of data. Chi-square test was used for comparison of the differences between groups. The scars Vancouver scores were analyzed by one-way ANOVA two years after treatment. P<0.05 was considered statistically significant. Results The absorption rate in the proliferative phase of the dynamic treatment group was (4.32±0.00) cGy.s-1.cm-2, which was higher than that in the traditional treatment group (3.24±0.00) cGy.s-1.cm-2(F=1.742, P=0.000). Two years after treatment, the score in the dynamic treatment group was (2.94±1.22) points, which was lower than that in the traditional treatment group (4.21±1.68) (F=93.841, P=0.000); the complication rate and recurrence rate were 0.9% (3/323) and 0.6% (2/323) in dynamic treatment group while 11.1% (28/252) and 9.5% (24/252) in traditional treatment group, respectively. The difference was statistically significant(χ2=457.69, P=0.000; χ2=457.70, P=0.000). Conclusions The treatment of surgery combined with 90Sr isotope is effective in pathological scar, but treatment programs should be developed as a dynamic treatment according to the individual characteristics of the pathological scar. Key words: Hypertrophic scar; Keloid; 90Sr
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".