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Record W3092512076 · doi:10.17116/hirurgia202009151

Multicenter study of the effectiveness of antiscar therapy in patients at different age periods

2020· article· en· W3092512076 on OpenAlexaboutno aff
Sergey Minaev, Oksana Vladimirova, Igor Kirgizov, M.A. Akselrov, Maxim Razin, A A Ivchenko, Sergey I. Timofeev, Tarakanov Va, Н. К. Барова, А. Н. Обедин, M.V. Zelenskaya

Bibliographic record

VenuePirogov Russian Journal of Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRating scalePediatricsPhysical therapySurgeryPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Was to evaluate the effectiveness of anti-scar treatment with Contractubex gel in children and adults. MATERIAL AND METHODS: A group of researchers based on clinical hospitals and university medical clinics carry out the multicenter study to evaluate the effectiveness of anti-scar treatment with Contractubex gel containing cepalin, allantoin and heparin, with its early appointment in groups of children from 12 to 18 years old and adults from 21 to 35 years old. The study included data from 216 patients. Patients of both age groups were initially divided into two: the main and control ones with an equal distribution according to the type of surgical intervention (hernia repair and appendectomy), age, gender, and anamnestic data. The dynamic observation was carried out using two rating scales - filled out by a doctor (Vancouver scale) and a patient (author's rating scale in the Scar Diary mobile application). RESULTS: <0.05) improvement in the main group (0,2±0,06 points) compared with the control group (0,6±0,17 points). In addition, was noticed the strong commitment to anti-scar treatment in pediatric patients. CONCLUSIONS: The work confirms the undoubted need for anti-scar treatment in the early stages of scar formation after surgical interventions, which accelerates the psychophysical rehabilitation of patients after surgery and improves the quality of life.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.286

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 head0.033
GPT teacher head0.265
Teacher spread0.232 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

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