Study of the Dosing Tissue Distraction Clinical Efficacy in the Soft Tissue Defects Treatment of Various Etiologies in the Lower Extremities
Bibliographic record
Abstract
Justification The most methods of extensive skin and soft tissue defects are aimed at accelerating wound healing and preventing infectious complications. To improve the effectiveness of such defects treatment, a method of dosed tissue distraction (MDTD) is used, consisting in the application of a continuously acting load to the area of healthy soft tissue in close proximity to the wound defect. Purpose It performed the evaluation of the medico-social effectiveness of the introduction into clinical practice of developed methods and devices for implementing MDTD in the treatment of skin and soft tissue defects of the extremities. Methods 407 patients were treated with wound defects of the extremities, which were divided into two groups: the main group – 198 patients in whose treatment MDTD was applied using original methods and devices; comparison group – 209 patients, in whose treatment standard treatment methods were applied. Comparison of the long-term results of treatment according to the frequency of repeated operations, complications, indicators of quality of life, frequency of disability. Results The use of MDTD is characterized by better performance compared with the use of standard approaches. There is a decrease in the frequency of performing reconstructive plastic surgery after inpatient treatment (9-10 times), remote complications by 2.6 times, a reduced value of the Vancouver scale (by 28.8%), quality of life indicators higher levels. The use of the proposed approach is characterized by a shorter duration of treatment (by 26.0%), duration of disability (1.4 times), cases of disability (2.2 times). Conclusion The use of MDTD is characterized by high medical and social efficiency, allows to reduce the cost of treating extensive skin and soft tissue defects by reducing the length of hospitalization, the frequency of repeated rehabilitation and reconstructive operations, accelerated recovery of patients, improving the quality of life and reducing the incidence of disability
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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 | 0.001 |
| 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".