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Record W2936265289 · doi:10.17709/2409-2231-2019-6-1-5

ANALYSIS OF THE INTRAOPERATIVE ULTRASOUND RESULTS IN THE SURGICAL TREATMENT OF RENAL TUMORS

2019· article· en· W2936265289 on OpenAlexaff
A. D. Karnin, А. А. Костин, С. О. Степанов, N. V. Vorobyev, П. Д. Беспалов, V Dimitrov

Bibliographic record

VenueResearch and Practical Medicine Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsNational Defence Medical Centre
Fundersnot available
KeywordsMedicineNephrectomyKidneyKidney cancerUltrasonographySurgeryUltrasoundRadiologyLesionCancerRenal tumorInternal medicine

Abstract

fetched live from OpenAlex

Purpose . To evaluate the effectiveness of intraoperative ultrasonography (IOUS) in the surgical treatment of kidney tumors. Patients and methods . Possibilities of IOUS application in the surgical treatment of kidney tumor according to the results of examination and surgical treatment of 145 patients (95 men and 50 women) diagnosed with kidney cancer were evaluated. The patients were divided into 2 groups: group 1 (comparisons) — 76 patients; during the examination and treatment, the IOUS method was not used, group 2 (basic) — 69 patients, who during the surgical treatment used the IOUS method to further clarify the localization, the size and boundaries of the tumor formation. Results . The use of IOUS helped to reduce the frequency of nephrectomy (11.9% in the comparison group and 8.7% in the main group), and reduce the frequency of intraoperative complications, which amounted to (8.7% against 13.2%). The frequency of damage to the spleen and the transition to open surgery did not differ. The use of IOUS allowed to characterize in detail the anatomical features of the tumor, its vascularization and to implement the RENAL prognosis: favorable — in 50.7% of patients, unfavorable — in 15.9%. Conclusion .When performing kidney tumor surgery, it is recommended to perform IOUS of the affected kidney in order to further clarify the boundaries of the tumor lesion to improve the effectiveness of the operation.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.080
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.121
GPT teacher head0.444
Teacher spread0.323 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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