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Silver-coated nylon dressings for pediatric burn victims

2007· article· en· W4240988956 on OpenAlexaffabout
Daniel E. Borsuk, Michel Gallant, D Smith Richard

Bibliographic record

VenuePlastic Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsMontreal Children's HospitalChild, Adolescent and Family Mental HealthMcGill University Health CentreMcGill University
Fundersnot available
KeywordsPediatric burnMedicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Silver dressings are a proven method for burn treatment. Current challenges associated with burn treatment include pain management and limited hospital resources. A new silver-coated nylon dressing was used at the Montreal Children's Hospital (Montreal, Quebec) to help reduce traumatic dressing changes and cost. METHODS: Burn victims in a pediatric patient population were followed over two years. Patients were excluded if they were evaluated more than 48 h postburn or if the burn affected less than 5% of the total body surface area. The same burn team admitted and treated all case subjects, and one dressing nurse recorded and monitored all progress throughout the study to ensure standardization. RESULTS: Fifteen patients were included in the study. The average number of dressing changes needed was 4.13, with a median of three changes. The average total body surface area burned was 8%, with a mean of 13.9 days before superficial wounds were re-epithelialized. The average length of in-hospital stay was four days. The cost was $388 less for silver-coated nylon dressings than for silver sulfadiazine cream for seven days of treatment. Silver-coated nylon dressings did not leave any residue or pseudoeschar on the wounds and were easily maintained at home. CONCLUSION: The silver-coated nylon dressings are as effective as other silver dressings used for pediatric burn victims. The dressings are less traumatic, require fewer resources and do not leave wound residue compared with other dressings.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.013
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.029
GPT teacher head0.280
Teacher spread0.251 · 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

Citations5
Published2007
Admission routes2
Has abstractyes

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