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Record W2091461537 · doi:10.1002/lt.20231

Prospective evaluation of the role of quantitative Doppler ultrasound surveillance in liver transplantation

2004· article· en· W2091461537 on OpenAlexaff
David Stell, Dónal B. Downey, Paul Marotta, Edward Solano, Anand Khakhar, Douglas Quan, Cam Ghent, Vivian C. McAlister, William Wall

Bibliographic record

VenueLiver Transplantation · 2004
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineLiver transplantationCirrhosisAnastomosisResistive indexBlood flowTransplantationUltrasoundDoppler ultrasoundDoppler effectRadiologyVascular resistanceHemodynamicsSurgeryArteryCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Doppler ultrasound (DUS) is able to measure parameters of blood flow within vessels of transplanted organs, and vascular complications are associated with abnormal values. We analyzed the findings of 51 consecutive patients who underwent DUS on 2 occasions in the first postoperative week following liver transplantation for cirrhosis to determine the range of values in patients following liver transplantation. Three patients developed early vascular thromboses that were detected by the absence of a Doppler signal. In patients making an uneventful recovery, the arterial velocity tended to increase and the resistive index (RI) to decrease during the first postoperative week. All recipients were shown to have high-velocity segments within the hepatic artery, without an increase in flow resistance. Assessment of the portal vein revealed narrowing at the anastomosis, associated with a segmental doubling of flow velocity, and the mean portal venous flow decreased by approximately 20% in the first postoperative week. In conclusion, a wide range of abnormalities occurs in the vessels of liver transplant recipients, which were not associated with the development of vascular complications or affect patient management.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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

Citations66
Published2004
Admission routes1
Has abstractyes

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