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Record W2807416391 · doi:10.1111/iwj.12921

The application of incisional negative pressure wound therapy for perineal wounds: A systematic review

2018· review· en· W2807416391 on OpenAlexaff
Caitlin Cahill, Amanda Fowler, Lara Williams

Bibliographic record

VenueInternational Wound Journal · 2018
Typereview
Languageen
FieldMedicine
TopicAnorectal Disease Treatments and Outcomes
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineNegative-pressure wound therapyAbdominoperineal resectionWound dehiscenceWound healingSurgeryDehiscencePerineumSurgical woundCancerColorectal cancerInternal medicine

Abstract

fetched live from OpenAlex

Impaired perineal wound healing is a major source of morbidity after abdominoperineal resection. Incisional negative pressure wound therapy can improve healing, prevent infections, and decrease the frequency of dehiscence. Our objective was to summarise existing evidence on the use of incisional negative pressure wound therapy on perineal wounds after abdominoperineal resection and to determine the effect on perineal wound complications. Electronic databases were searched in January 2017. Studies describing the use of incisional negative pressure wound therapy on primarily closed perineal wounds after abdominoperineal resection were included. Of the 278 identified articles, 5 were retrieved for inclusion in the systematic review (n = 169 patients). A significant decrease in perineal wound complications when using incisional negative pressure wound therapy was demonstrated, with surgical site infection rates as low as 9% (vs 41% in control groups). The major limitation of this systematic review was a small number of retrieved studies with small patient populations, high heterogeneity, and methodological issues. This review suggests that incisional negative pressure wound therapy decreases perineal wound complications after abdominoperineal resection. Further prospective trials with larger patient populations would be needed to confirm this association and delineate which patients might benefit most from the intervention.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.797
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.401
Teacher spread0.362 · 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 designSystematic review
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

Citations29
Published2018
Admission routes1
Has abstractyes

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