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Record W1554428559

Management of open abdominal wounds with a dynamic fascial closure system.

2008· article· en· W1554428559 on OpenAlexaff
Mark W Reimer, Jean-Denis Yelle, Bert J Reitsma, Gaby Doumit, Murray A Allen, Michael Bell

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineClosure (psychology)SurgeryGeneral surgery
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: In damage-control surgery, definitive abdominal closure may not be possible for several days or weeks after laparotomy until the patient has stabilized. METHODS: We present 23 patients treated with the Canica ABRA dynamic wound closure system that re-approximated open abdomens with silicone elastomers placed transfascially across the wound. This study aimed to assess the results of using this system and to identify risk factors for unsuccessful closure. The system maintains a medially directed force across the wound. A traditional regimen of wound dressing changes was performed. RESULTS: The dynamic closure system remained in place an average of 48 days and was applied an average of 18 days after the beginning of treatment for the open abdominal wound. Delayed primary fascial closure was achieved in 14 of 23 patients (61%) without further surgery. Six patients (26%) healed with ventral hernias but with a smaller abdominal defect. Two patients (9%) developed enterocutaneous fistulae through the wound that required further surgery. An overall reduction in wound area of 95% was achieved. CONCLUSION: This dynamic wound closure technique permitted the delayed primary closure of open abdomens in 61% of cases when treatment was instituted an average of 18 days after initial laparotomy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2008
Admission routes1
Has abstractyes

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