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Record W2352100956

Diagnosis of posterior nutcracker syndrome by color Doppler ultrasound

2012· article· en· W2352100956 on OpenAlexaff
Luo Xiao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicVascular anomalies and interventions
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsMedicineUltrasoundDoppler effectInferior vena cavaNutcracker syndromeLeft renal veinRadiologyColor dopplerDoppler ultrasoundHemodynamicsAortaAnatomyUltrasonographyCardiology
DOInot available

Abstract

fetched live from OpenAlex

Objective To investigate the value of color Doppler ultrasound in the diagnosis of posterior nutcracker syndrome(PNCS)by analyzing the ultrasonographic data.Methods Eight cases diagnosed as PNCS according to history and examinations results were performed color Doppler ultrasound.The morphology and pathway of the left renal vein(LRV)were observed.After that,the anteroposterior diameter of LRV and peak velocity were measured both at the dialated portion on the left side of aorta and the narrowest segment between aorta and lumber vertebral.The ratios of both the diameter and the peak velocity were calculated later.Results Color Doppler ultrasound can clearly display the pathway of LRV running into the inferior vena cava.In all the 8 cases, LRV was demonstrated to flow into the inferior vena cava retroaortically by color Doppler ultrasound in multiple scanning planes.The diameter of the narrowest portion of the LRV was (1.5±0.4)mm with the peak velocity of (154.5±30.1)cm/s,whereas the diameter of the dilated portion was (8.7±1.4)mm with the peak velocity of (22.8±3.4)cm/s.The ratio of the diameter and the peak velocity between the narrowest portion with the dilated portion of the LRV were 1:(6.5±0.5)and 1:(7.2±0.9)respectively.Conclusion Color Doppler ultrasound could evaluate accurately the hemodynamic changes due to left renal vein anomalies and is useful for the diagnosis and management of PNCS.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.988

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)0.0130.000

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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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