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Comparison of Silicone Sheets and Paper Tape for the Management of Postoperative Scars: A Randomized Comparative Study

2020· article· en· W3026957376 on OpenAlexaboutno aff
Ying-Sheng Lin, Pei‐San Ting, Kuei‐Chang Hsu

Bibliographic record

VenueAdvances in Skin & Wound Care · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsVisual analogue scaleSiliconeSurgeryRandomized controlled trial

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the effectiveness of silicone sheets and paper tape in the prevention of postoperative cesarean section scars. METHODS: Patients undergoing horizontal cesarean section were included in this randomized controlled trial. Surgical wounds were divided into two halves. Patients randomly applied silicone sheets or paper tape to each side of their wound as assigned for 3 months. Wounds were assessed at 1, 3, 6, and 12 months after surgery. Researchers used the objective Vancouver Scar Scale (VSS) to evaluate the scars and the subjective visual analog scale (VAS) to evaluate itch, pain, and scar appearance. RESULTS: No significant differences between the silicone sheet and paper tape groups were noted at postoperative follow-ups with respect to VSS scores. The silicone sheet group had significantly better VAS scores for scar appearance than the paper tape group at 6 (6.81 ± 1.47 vs 6.19 ± 1.62, P = .03) and 12 (6.88 ± 2.01 vs 6.2 ± 2.08, P = .04) months' follow-up, respectively. CONCLUSIONS: The silicone sheet group showed statistically significant differences in comparison with the paper tape group in terms of scar appearance as determined by the VAS. However, the differences were too small to be clinically meaningful.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.029
GPT teacher head0.375
Teacher spread0.346 · 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 designQualitative
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

Citations16
Published2020
Admission routes1
Has abstractyes

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