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Record W2012586723 · doi:10.12968/jowc.2007.16.4.27028

Management of bioburden with a burn gel that targets nociceptors

2007· article· en· W2012586723 on OpenAlexaffabout
Lucie Martineau, H. M. Dosch

Bibliographic record

VenueJournal of Wound Care · 2007
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of TorontoDefence Research and Development Canada
Fundersnot available
KeywordsBioburdenMedicinePseudomonas aeruginosaContext (archaeology)HygieneSurgeryBacteriaPathology

Abstract

fetched live from OpenAlex

Aim: To assess the effectiveness of RescuDerm, an amorphous, water-soluble burn gel in controlling Pseudomonas aeruginosa growth in rat full-thickness wounds contaminated with 10 3 , 10 5 or 10 7 CFU/g tissue. Method: Wounds were treated daily for 72 hours with a placebo gel, a 5% w/w mafenide acetate gel (Maf), or with four modalities of RescuDerm application. Results: All RescuDerm treatments were equally effective within 24 hours in preventing further Pseudomonas aeruginosa growth in wounds contaminated with 10 3 CFU/g tissue. Pseudomonas aeruginosa levels remained at or below this baseline count for 72 hours in all but one of the RescuDerm treatments. The bioburdens in Maf-treated wounds were negligible, averaging 0.14 ± 0.09 log 10 CFU/g tissue. While RescuDerm and Maf remained bacteriostatic in wounds contaminated with 10 5 CFU/g tissue, this property disappeared at higher bioburdens. Conclusion: RescuDerm can be used for the management of cutaneous injuries sustained in environments deemed marginally or moderately contaminated. Heavily contaminated wounds would require irrigation prior to application to reduce their bioburden below 10 5 CFU/g tissue. Declaration of interest: This work was partly supported by NociPharm. Defence Research and Development Canada (DRDC) Toronto designed all the experimental protocols. The findings of this study were not constrained by the sponsoring body, and have never been presented at a scientific meeting or in another public context.

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 score0.114
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.294
Teacher spread0.279 · 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

Citations6
Published2007
Admission routes2
Has abstractyes

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