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Record W2620687151 · doi:10.1590/0100-69912017002002

Impact of non-adherent Ibuprofen foam dressing in the lives of patients with venous ulcers

2017· article· en· W2620687151 on OpenAlexaboutno aff
Geraldo Magela Salomé, Lydia Masako Ferreira

Bibliographic record

VenueRevista do Colégio Brasileiro de Cirurgiões · 2017
Typearticle
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIbuprofenMcGill Pain QuestionnaireProspective cohort studyLower limbSurgeryPhysical therapyVisual analogue scaleAnesthesia

Abstract

fetched live from OpenAlex

Objective: to evaluate pain in patients with lower limb venous ulcer who used non-adherent Ibuprofen foam dressing (IFD). Methods: we conducted a prospective study of patients with lower limb venous ulcers treated from April 2013 to August 2014. We used the Numerical Scale and McGill Pain Questionnaire, performing the assessments at the moment of inclusion of the patient in the study and every eight days thereafter, totaling five consultations. We divided the patients into two groups: 40 in the Study Group (SG), who were treated with IFD, and 40 in the Control Group (CG), treated with primary dressing, according to tissue type and exudate. Results: at the first consultation, patients from both groups reported intense pain. On the fifth day, SG patients reported no pain and the majority of CG reported moderate pain. Regarding the McGill Pain Questionnaire, most patients of both groups reported sensations related to sensory, affective, evaluative and miscellaneous descriptors at the beginning of data collection; after the second assessment, there was slight improvement among the patients in the SG. After the third consultation, they no longer reported the mentioned descriptors. CG patients displayed all the sensations of these descriptors until the fifth visit. Conclusion: non-adherent Ibuprofen foam dressing is effective in reducing the pain of patients with venous ulcers.

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 score0.008
Threshold uncertainty score0.562

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 head0.019
GPT teacher head0.326
Teacher spread0.307 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueRevista do Colégio Brasileiro de CirurgiõesSame topicDiagnosis and Treatment of Venous DiseasesFrench-language works237,207