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Record W3199260252 · doi:10.1097/mcc.0000000000000879

Management of the patient with the open abdomen

2021· article· en· W3199260252 on OpenAlexaff

Bibliographic record

VenueCurrent Opinion in Critical Care · 2021
Typearticle
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsAbdomenSedationClosure (psychology)Conservative managementAbdominal compartment syndromeAcute abdomen

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this study was to outline the management of the patient with the open abdomen. RECENT FINDINGS: An open abdomen approach is used after damage control laparotomy, to decrease risk for postsurgery intra-abdominal hypertension, if reoperation is likely and after primary abdominal decompression.Temporary abdominal wall closure without negative pressure is associated with higher rates of intra-abdominal infection and evisceration. Negative pressure systems improve fascial closure rates but increase fistula formation. Definitive abdominal wall closure should be considered once oedema has subsided and the patient has stabilized. Delayed abdominal closure after trauma (>24-48 h) is associated with less achievement of fascial closure and more complications. Protective lung ventilation should be employed early, particularly if respiratory compromise is evident. Conservative fluid management and less sedation may decrease delirium and increase definitive abdominal closure rates. Extubation may be performed before definitive abdominal closure in selected patients. Antibiotic therapy should be brief, targeted and guideline concordant. Survival depends on the underlying disease, the closure method and the course of hospitalization. SUMMARY: Changes in the treatment of patients with the open abdomen include negative temporary closure, conservative fluid management, early protective lung ventilation, decreased sedation and extubation before abdominal closure in selected patients.

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 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 score0.630
Threshold uncertainty score0.121

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)0.0000.000

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 head0.144
GPT teacher head0.435
Teacher spread0.292 · 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
Published2021
Admission routes1
Has abstractyes

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