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Record W2971647905 · doi:10.1097/dss.0000000000002145

Quantification of Erythema Associated With Continuous Versus Interrupted Nylon Sutures in Facial Surgery Repair: A Randomized Prospective Study

2019· article· en· W2971647905 on OpenAlexaff
Ardalan Akbari, David Zloty

Bibliographic record

VenueDermatologic Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineSurgeryRandomized controlled trialErythemaProspective cohort study

Abstract

fetched live from OpenAlex

BACKGROUND: Patients are often concerned about the cosmetic appearance of scars following Mohs micrographic surgery (MMS), including residual erythema. However, few studies have compared the cosmetic outcomes between different suturing techniques. OBJECTIVE: To compare the erythema intensity (EI) associated with interrupted sutures (IS) and continuous sutures (CS), and the degree of its reduction over time. MATERIALS AND METHODS: Mohs micrographic surgery patients were randomized to have half of their defect repaired with IS and the other half with CS. Postoperatively, subjects were assessed at 1 week, 2 months, and 6 months and close-up photographs of their scars were taken. Computer-assisted image analysis was utilized to quantify the EI in each half-scar. RESULTS: The average EI of IS was greater than that of CS by 9.3% at 1 week (p < .001) and 7.2% at 2 months (p < .021) but comparable at 6 months. These differences were clinically detectable, but EI differences resolved by 6 months in most cases. At 6 months, EI regressed by 33.5% in IS and 26.3% in CS. CONCLUSION: Continuous sutures are associated with less erythema during early scar maturation but are comparable to IS at 6 months. These results may guide the choice of suturing technique to improve early cosmetic outcomes and overall patient satisfaction.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.280
Teacher spread0.248 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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