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Record W3049484745 · doi:10.1097/dss.0000000000002642

Modified Buried Vertical Mattress Suture Versus Buried Intradermal Suture: A Prospective Split-Scar Study

2020· article· en· W3049484745 on OpenAlexaboutno aff
Zonghui Liu, Zhishui Tang, Xiaoyan Hao, Xiangyu Liu, Lin He, Xueyuan Yu, Rui Wang, Youcheng He, Yuan Guo, Maoguo Shu

Bibliographic record

VenueDermatologic Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFibrous jointVisual analogue scaleSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The modified buried vertical mattress suture (MBVMS) is believed to provide excellent outcomes by relieving the tension on wound edges. However, clinical data on the topic remain sparse and inadequate. OBJECTIVE: To compare the cosmetic results of the MBVMS and the buried intradermal suture (BIS) in chest wounds using a split-scar model. MATERIALS AND METHODS: Twenty patients participated in the study. One randomly selected half of each chest wound was closed with the MBVMS; the other half was closed with the BIS. Immediately, postoperatively, the maximum degree of wound eversion was obtained. After 3 months, the wound complication rates were recorded, and the aesthetic appearance of each scar was evaluated by the Patient and Observer Scar Assessment Scale (POSAS), the Vancouver Scar Scale (VSS), the visual analog scale (VAS), and scar width. RESULTS: The MBVMS yielded a greater mean postoperative eversion height and width (p < .05); lower POSAS, VSS, and VAS scores (p < .05); and a narrower scar width (p < .05) than did the BIS. CONCLUSION: Compared with the BIS, the MBVMS provided significantly increased wound eversion immediately, postoperatively, and improved aesthetic outcomes at the end of the 3-month follow-up period.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.073
GPT teacher head0.291
Teacher spread0.218 · 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 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

Citations19
Published2020
Admission routes1
Has abstractyes

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