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Record W2524808901 · doi:10.1177/1553350616671640

The Abdominal Reapproximation Anchor Device

2016· article· en· W2524808901 on OpenAlexaboutno aff
Alfin Okullo, Mehan Siriwardhane, Tony Pang, Jane‐Louise Sinclair, Vincent Lam, A. J. Richardson, Henry Pleass, Emma Johnston

Bibliographic record

VenueSurgical Innovation · 2016
Typearticle
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryAbdominal compartment syndromeAbdomenIntensive care unitMedical recordIntensive care medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Achieving primary fascial closure after damage control laparostomy can be challenging. A number of devices are in use, with none having yet emerged as best practice. In July 2013, at Westmead Hospital, we started using the abdominal reapproximation anchor (ABRA; Canica Design, Almonte, Ontario, Canada) device. We report on our experience. METHODS: A retrospective review of medical records for patients who had open abdomens managed with the ABRA device between July to December 2013 was done. Data extracted included age, sex, body mass index (BMI), reason for the open abdomen, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, number of laparostomies prior to ABRA placement, duration of placement, device complications, length of hospital and intensive care unit (ICU) stay, and outcomes. RESULTS: , APACHE II score was 14.5, duration with open abdomen prior to ABRA placement was 11.75 days, duration with ABRA in situ was 9 days, duration of hospital stay was 64.25 days, and ICU stay was 37.75 days. Three patients (75%) achieved fascial closure, and 1 achieved skin closure. No incidences of enterocutaneous fistulae occurred. CONCLUSION: The ABRA is a unique emerging alternative to aid in achieving fascial closure in patients managed with open abdomens. Our case series demonstrates that it can be used effectively in selected patients. Studies are needed to compare its efficacy with more traditional methods.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.039
GPT teacher head0.321
Teacher spread0.282 · 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 designNot applicable
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

Citations11
Published2016
Admission routes1
Has abstractyes

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