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Record W3027721872 · doi:10.1111/iwj.13405

Rapid enzymatic burn debridement: A review of the paediatric clinical trial experience

2020· review· en· W3027721872 on OpenAlexaboutno aff
Yaron Shoham, Yuval Krieger, Guy Rubin, Ingo Koenigs, Bernd Hartmann, Frank Sander, Alexandra Schulz, Keren David, Lior Rosenberg, Eldad Silberstein

Bibliographic record

VenueInternational Wound Journal · 2020
Typereview
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
FundersMediWound
KeywordsEscharMedicineIncidence (geometry)Debridement (dental)SurgeryClinical trialRandomized controlled trialAdverse effectTotal body surface areaInternal medicine

Abstract

fetched live from OpenAlex

NexoBrid (NXB) has been proven to be an effective selective enzymatic debridement agent in adults. This manuscript presents the combined clinical trial experience with NXB in children. Hundred and ten children aged 0.5 to 18 years suffering from deep thermal burns of up to 67% total body surface area were treated with NXB in three clinical trials. Seventy-seven children were treated with NXB in a phase I/II study, where 92.7% of the areas treated achieved complete eschar removal within 0.9 days from admission. Thirty-three children (17 NXB, 16 standard of care [SOC]) participated in a phase III randomized controlled trial. All wounds treated with NXB achieved complete eschar removal. Time to complete eschar removal (from informed consent) was 0.9 days for NXB vs 6.5 days for SOC (P < .001). The incidence of surgical excision was 7.9% for NXB vs 73.3% for SOC (P < .001). Seventeen of these children participated in a phase III-b follow-up study (9 NXB and 8 SOC). The average long-term modified Vancouver Scar Scale scores were 3.4 for NXB-treated wounds vs 4.4 for SOC-treated wounds (NS). There were no significant treatment-related adverse events. Additional studies are needed to strengthen these results.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.149
GPT teacher head0.452
Teacher spread0.303 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations37
Published2020
Admission routes1
Has abstractyes

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