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Record W4415900530 · doi:10.12669/pjms.41.11.12555

Efficacy and safety of vacuum sealing drainage combined with skin grafting for the treatment of limb burns

2025· article· en· W4415900530 on OpenAlexaboutno aff
Liyong Zhu, Shiqing Zheng, Panyong Li, Pei Liu

Bibliographic record

VenuePakistan Journal of Medical Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSkin graftingWound healingGraftingSAFERDrainageLower limb

Abstract

fetched live from OpenAlex

Objective: Exploring the efficacy and safety of vacuum sealing drainage (VSD) combined with skin grafting for limb burns. Methodology: This retrospective cohort study collected clinical records of 140 patients with limb burns who underwent skin grafting surgery in Yongkang First People's Hospital from November, 2021 to October, 2024. Among them, 70 patients who received the first stage VSD combined with the second stage skin grafting treatment (VSD group) were matched in a 1:1 ratio with the queue of patients who received a traditional dressing change treatment combined with the second stage skin grafting treatment (conventional group). The healing time, granulation growth time, length of hospital stay, scar hyperplasia (as assessed by the Vancouver Scar Scale, VSS), degree of pain and incidence of complications were compared. Results: The healing time, granulation growth time, length of hospital stay and VSS score of the VSD group were lower than those of the conventional group (P<0.05). The VAS scores of the VSD group were lower compared to the conventional group at one, three and seven days after treatment (P<0.05). VSD combined with skin grafting was associated with a significantly lower (18.6%) incidence of complications than the conventional treatment (34.3%) (P<0.05). Conclusions: VSD combined with skin grafting is safer than the traditional treatment approach and is more efficient in shortening the wound healing process and reducing pain and scar formation in patients with limb burns.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.378
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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