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Record W3011379886 · doi:10.1155/2020/6162158

Kidney Salvage with Renal Artery Reconstruction after Blunt Traumatic Injury

2020· article· en· W3011379886 on OpenAlexaff
David G. Jackson, Phillipe Abreu, Manuel Anthony Moutinho, Antonio Marttos, George W. Burke, Nicholas Namias, Gaetano Ciancio

Bibliographic record

VenueCase Reports in Urology · 2020
Typearticle
Languageen
FieldMedicine
TopicAbdominal Trauma and Injuries
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSurgeryRevascularizationBluntRenal functionRenal arteryNephrectomyAbdominal traumaKidneyExploratory laparotomyRadiologyBlunt traumaCardiologyMyocardial infarctionInternal medicine

Abstract

fetched live from OpenAlex

Renal artery injury from blunt abdominal trauma is a rare condition that is typically managed nonoperatively in hemodynamically stable patients. Revascularization can be achieved by stenting or surgical reconstruction of the renal artery. All attempts at revascularization should minimize warm ischemic time. Here, we discuss a patient postmotor vehicle accident who presented to Ryder Trauma Center with intra-abdominal bleeding. He underwent emergency exploratory laparotomy with splenectomy and abdominal packing. Postoperative CT scan revealed a contrast nonenhancing left kidney. The patient then returned to the operating room and underwent in situ renal artery reconstruction after >4 hours of warm ischemia. The patient survived a 2-month hospital course and was discharged home after prolonged in-hospital stay and intensive care treatment. Nuclear medicine scan showed scarring and atrophy of the reattached kidney with 16.3% of overall function attributed to the affected kidney. This case shows that patients with renal artery injury can be managed operatively with arterial reconstruction. Reducing warm ischemic time is critical in preserving kidney function.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.519

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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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 designCase report
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

Citations4
Published2020
Admission routes1
Has abstractyes

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