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Record W2892714485 · doi:10.4103/jrms.jrms_681_17

Effect of extracorporeal shock wave therapy on improving burn scar in patients with burnt extremities in Isfahan, Iran

2018· article· en· W2892714485 on OpenAlexaboutno aff
Mahsa Mazaheri, Parisa Taheri, Saeid Khosrawi, MehrdadAdib Parsa, Arghavan Mokhtarian

Bibliographic record

VenueJournal of Research in Medical Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineExtracorporeal shock wave therapyItchingScarsVisual analogue scaleSurgeryExtracorporeal shockwave therapyBurn injuryProspective cohort studyPain scoreAnesthesia

Abstract

fetched live from OpenAlex

Background: Pathologic scarring is a common problem after burn injury that has functional and esthetic limitations. Conservative and surgical treatments available for these scars are not always satisfactory. Extracorporeal shock wave therapy (ESWT) is a noninvasive modality that has proven positive effects on burn scars and wound healing in few studies. This study was conducted to evaluate the effects of ESWT on improving burn scar in extremities. Materials and Methods: This study was a prospective quasi-experimental on burn patients with burn scar in their extremities that underwent ESWT sessions once a week for 6 weeks. For evaluating pain and itching, visual analog scale (VAS) and for scar appearance, Vancouver Scar Scale (VSS) were used. These scales were measured and compared at the beginning of the treatment, at the end of the treatment, and 1 and 3 months after the end of the intervention. Results: In this study, 17 patients were treated with ESWT with a mean age of 37.94 ± 7.25 years that 47.1% of them were male. The mean of VAS score for pain and itching and VSS score were decreased significantly after the treatment and during follow-ups (All P < 0.001). Conclusion: ESWT can improve the pain, itching, and appearance of the burn scar in human extremities in burn patients.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.241
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.075
GPT teacher head0.412
Teacher spread0.337 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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