MétaCan
Menu
Back to cohort
Record W3017985110 · doi:10.15562/bmj.v8i3.1556

Intradermal suture effect using polypropylene materials on post-operative scar tissue in cases of lower extremities closed fracture cases

2019· article· en· W3017985110 on OpenAlexaboutno aff
Decky Ario, Herman Yosef Limpat Wihasetyoko, Hery Susilo

Bibliographic record

VenueBali Medical Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryFibrous jointFixation (population genetics)Internal fixation

Abstract

fetched live from OpenAlex

Introduction: Hypertrophic scarring is the most common complication in postoperative wounds. One of the preventive measures for scarring is by adding intradermal sutures when closing the surgical wound. This study aims to prove that the addition of intradermal sutures using polypropylene materials can reduce the complications of scarring in postoperative wounds on lower extremities closed fracture cases under internal fixation procedure.Method: Experimental studies using Randomized Controlled Trial Post Test Only Design were carried out in patients with lower extremity fractures in Saiful Anwar General Hospital, Malang. The sample was chosen by proportional sampling which was the group given the treatment as well as the control group (n = 36). The variable measured was the clinical appearance of scar tissue formed 6 months after surgery using the Vancouver Scar Scale.Result: The results showed that the addition of intradermal sutures using polypropylene materials had a significant effect on the formation of postoperative scar tissue. The total Vancouver Scar Scale score has a value of p = 0.000 (α = 5%) for Mann Whitney test.Conclusion: The conclusion of this study is that the addition of intradermal sutures using polypropylene threads in cases of closed fractures with internal fixation can reduce the appearance of postoperative scar tissue.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0100.000

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 head0.014
GPT teacher head0.317
Teacher spread0.304 · 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.

Study designBench or experimental
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBali Medical JournalSame topicSurgical Sutures and AdhesivesFrench-language works237,207