Early excision and grafting versus delayed grafting in deep burns of the hand
Bibliographic record
Abstract
Background: Deep burns of the hand are considered severe, because even a small wound may cause profound functional disability, ugly scar and psychosocial problems. The aim of the study is to compare early excision and grafting versus delayed grafting in deep burns of the hand.Methods: This study was conducted on 30 patients with deep burns of the hand. Patients were randomly divided in to two equal groups. Group I included 15 patients who were subjected to early excision and grafting within the first week after injury while group II included 15 patients who were subjected to delayed excision and grafting two weeks after injury. The study was conducted on patients presented to Plastic and Reconstructive Surgery Department of Abou Qir General Hospital in the period from December 2016 to December 2017.Results: The results of early excision and grafting were better than delayed grafting regarding the intake, infection and post-burn contraction (mean 91.33±7.67 in group I and 83.67±10.08 in group II with p value=0.026).Conclusions: Early excision and grafting of the hand is a better alternative than delayed excision and grafting as regards better graft intake, less wound infection, less contractures, less risk of regrafting, less hospital stay, less 5D-itching scale and Vancouver score and more cost effectiveness.
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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) | 0.001 | 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".