Clinical efficacy of a novel dosed tissue distraction method in the treatment of soft tissue defects in the lower limbs
Bibliographic record
Abstract
ИЗУЧЕНИЕ КЛИНИЧЕСКой ЭФФЕКТИвНоСТИ мЕТода доЗИроваННой ТКаНЕвой дИСТраКЦИИ ПрИ ЛЕЧЕНИИ дЕФЕКТов мяГКИХ ТКаНЕй раЗЛИЧНой ЭТИоЛоГИИ в оБЛаСТИ НИЖНИХ КоНЕЧНоСТЕй С. Н. Пятаков 1 , в. а.Порханов 2 , а. Г. Барышев 1 , С. Н. Пятакова 3 , С. а.Бардин 1 , И. в.Суздальцев 4 1 Кубанский государственный медицинский университет, Краснодар, российская Федерация 2 Научно-исследовательский институт -Краевая клиническая больница № 1 имени профессора С. в. очаповского, Краснодар, российская Федерация 3 Городская больница № 4, Сочи, российская Федерация 4 Ставропольский государственный медицинский университет, российская ФедерацияA comparative assessment of the original method of DTD for skin defects closure in the lower limbs compared with traditional approaches has been made.345 patients and injuries with skin and soft tissues defects of lower limbs were included in the analysis, out of which standard approaches were applied to the treatment of 164 patients, the original
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".