Contemporary surgical outcomes of venous tumour thrombectomy managed with intraoperative Doppler ultrasound for kidney cancer
Bibliographic record
Abstract
INTRODUCTION: Radical nephrectomy (RN) with venous tumour thrombectomy (VTT) carries a significant morbidity and mortality risk. Examination of a contemporary single-institution series permits the development of a management algorithm and an audit its results. We report outcomes following the use of intraoperative colour Doppler ultrasound and our surgical pathway. METHODS: We retrospectively reviewed the records of all patients who underwent RN with VTT for kidney cancer between January 1, 2013 and October 1, 2016. Surgical complications, postoperative complications (Clavien-Dindo classification ≥3), 90-day readmission rates, and outcomes are reported. Multivariate linear regression, logistic regression, and Cox proportional hazard modelling were used to identify associations. RESULTS: Fifty-eight patients underwent RN with VTT. Of these, 26 (45%) patients had Mayo Clinic level III or IV thrombus and nineteen required venovenous/cardiopulmonary bypass. Three patients required patch grafting. The median length of hospital stay was eight days and there were 20 major complications. The 30-day readmission rate was 21% and the 90-day mortality rate was 8.9%. In multivariate analysis, low serum albumin and age-adjusted Charlson comorbidity score predicted length of stay. Increased intraoperative blood loss was significantly associated with increasing body mass index, serum creatinine, tumour thrombus level, and a history of significant weight loss >9.1kg. Low serum hematocrit predicted 90-day mortality. CONCLUSIONS: Intraoperative colour Doppler ultrasound is a useful tool and can facilitate caval preservation. Caval grafting can be avoided in most cases. Venovenous bypass can be avoided in many level III cases. Early therapeutic anticoagulation should be instituted with caution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".