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Record W3160731614 · doi:10.1155/2021/6672591

Early Surgical Intervention Improves Survival in Acute Intestinal Ischemia in the Intensive Care Unit

2021· article· en· W3160731614 on OpenAlexaff
Hassan Bukhari, Anand Kumar

Bibliographic record

VenueBioMed Research International · 2021
Typearticle
Languageen
FieldMedicine
TopicAbdominal vascular conditions and treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineIntensive care unitIntervention (counseling)Intestinal ischemiaIschemiaIntensive care medicineInternal medicineReperfusion injuryNursing

Abstract

fetched live from OpenAlex

The study is aimed at assessing whether the early surgical intervention improves survival in acute mesenteric ischemia with septic shock. A retrospective study design was applied to review the charts of patients admitted to the intensive care unit. The data were collected through a review of the full patient chart including physician and nursing notes, pathology reports, intraoperative findings, CT findings, and endoscopy. The diagnosis of AMI for each patient was determined through clinical presentation/endoscopic visualization/laboratory results/radiographic imaging, surgical exam (tissue or visual) and/or autopsy. Death and survival were evaluated between short and long‐time‐interval for septic shock groups using the chi‐square test followed by calculating the P value. Total survival among the surgery group was 60 patients (95.24%) compared to 3 (4.76%) survival among patients who did not have surgery. The time from the onset of a shock to the time of surgical incision was calculated. The mean time to surgery was 17.7 hours. Total 65 patients (29.52%) had surgery between 4 and 12 hours from the onset of hypotension. Survivals among this group of patients were 41.7% ( n = 25). The survival difference was statistically significant than died patients with respect to the time of surgical intervention ( P = <0.001). Early removal of ischemic bowel in patients with AII‐related surgery has improved survival.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.425
Teacher spread0.345 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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