MétaCan
Menu
Back to cohort
Record W2904014103 · doi:10.1111/iwj.13045

Incisional negative pressure therapy reduces complications and costs in pressure ulcer reconstruction

2018· article· en· W2904014103 on OpenAlexaff
Anthony Papp

Bibliographic record

VenueInternational Wound Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineSurgeryNegative-pressure wound therapyProspective cohort studyComplicationDemographicsCohortAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Complications after pressure ulcer reconstruction are common. A complication rate of 21% to 58% and a 27% wound recurrence has been reported. The aim of this study was to decrease postoperative wound-healing complications with incisional negative pressure wound therapy (iNPWT) postoperatively. This was a prospective non-randomised trial with a historic control. Surgically treated pressure ulcer patients receiving iNPWT were included in the prospective part of the study (Treatment group) and compared with the historic patient cohort of all consecutive surgically treated pressure ulcer patients during a 2-year period preceding the initiation of iNPWT (Control). There were 24 patients in the Control and 37 in the Treatment groups. The demographics between groups were similar. There was a 74% reduction in in-hospital complications in the Treatment group (10.8% vs 41.7%, P = 0.0051), 27% reduction in the length of stay (24.8 vs 33.8 days, P = 0.0103), and a 78% reduction in the number of open wounds at 3 months (5.4 vs 25%, P = -0.0481). Recurrent wounds and history of previous surgery were risk factors for complications. Incisional negative pressure wound therapy shortens hospital stay, number of postoperative complications, and the number of recurrent open wounds at 3 months after reconstructive pressure ulcer surgery, resulting in significant cost savings.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.996

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)0.0040.000

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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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 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

Citations32
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Wound JournalSame topicSurgical site infection preventionFrench-language works237,207