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Record W4283794129 · doi:10.14228/jprjournal.v9i1.335

The Effectiveness of Intradermal Suture Technique on Hypertrophic Scar Prevention in Rats

2022· article· en· W4283794129 on OpenAlexaboutno aff
Herman Yosef Limpat Wihastyoko, Wicaksono Patria Wuryanjono

Bibliographic record

VenueJurnal Plastik Rekonstruksi · 2022
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryH&E stainIntradermal injectionFibrous jointHypertrophic scarWound healingStainingPathology

Abstract

fetched live from OpenAlex

Introduction : Hypertrophic scarring is a complication that occurs in post-surgical wounds. There are many ways to prevent scarring, but there have been no satisfactory methods yet. Moreover, no studies investigated the effectiveness of intradermal sutures. Polypropylene thread can be used to repair a scar after surgery. This study aimed to prove the effectiveness of intradermal sutures using polypropylene thread on surgical scar quality.Method : The study was conducted using a randomized, controlled trial, and post-test only with 20 rats (Rattus norvegicus) as animal subjects. The wounds were made on the back, approximately 6 mm x 2 cm. Then, the rats were divided into two groups: a control group without intradermal sutures (K) and treatment group with intradermal sutures (I). Histopathological examination using Hematoxylin and Eosin (H&E) staining was used to identify the fibroblast number on the 21st day. The fibrocytes number were also observed on the 12th week and the Vancouver Scar Scale (VSS) was used to assess the scar quality.Result : The number of fibroblasts and fibrocytes in the control group (K) was significantly smaller (53.60 ± 14.571; 3.20 ± 0.447) than the treatment (I) (243.20 ± 75.334; 171.40 ± 13.221). The VSS value was significantly greater in the control than the treatment.Conclusion: The intradermal sutures using polypropylene thread produced a better-quality scar after surgery compared to wounds without intradermal sutures.

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)0.001
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 score0.681
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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