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Record W2036428524 · doi:10.1016/j.ijsu.2010.12.005

Factors associated with positive outcomes in 131 patients treated with gauze-based negative pressure wound therapy

2010· article· en· W2036428524 on OpenAlexaff
Raymond M. Dunn, Theresa Hurd, Paul Chadwick, Julien Côté, John Cockwill, Trevor Mole, J. M. Smith

Bibliographic record

VenueInternational Journal of Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsMedicineNegative-pressure wound therapyGranulation tissueDiscontinuationWound healingSurgeryWound careExudateChronic woundPathology

Abstract

fetched live from OpenAlex

Negative Pressure Wound Therapy (NPWT) is commonly used in many surgical specialties to improve wound management and healing outcomes. This study reports the ability of gauze-based NPWT to address several treatment goals commonly defined at the onset of therapy. A prospective, multi-center, non-comparative clinical investigation was carried out using gauze-based NPWT in chronic and acute wounds. 131 patients including traumatic, post-surgical and chronic wounds were assessed. Weekly percentage reductions in wound area, depth and volume were 8.3%, 15.8% and 20.5% respectively (p < 0.001). A reduction in exudate level was observed from baseline to treatment discontinuation (p < 0.001). An increase (p = 0.007) in red granulation tissue and a decrease (p < 0.001) in non-viable tissue was observed. Baseline wound characteristics associated with slower rates of progress included chronic wound aetiologies, longer wound duration prior to NPWT and presence of diabetes as a co-morbidity. Important indicators of wounds which had improved sufficiently and no longer required NPWT included reduction in volume and exudate levels. Gauze-based NPWT can be used to address many of the treatment goals commonly defined at the onset of therapy including reduction in wound volume, management of exudate and infection status, and improvement in wound bed quality.

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)0.001
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 score0.005
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.032
GPT teacher head0.292
Teacher spread0.260 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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