Ultrasound Utilization in Hospitalized Kidney Transplant Recipients: Useful or Overused?
Bibliographic record
Abstract
Kidney transplant ultrasonography is an important diagnostic tool in the care of transplant recipients. This modality of nonradiation-based imaging allows for precise and expedient reporting of allograft architecture, which can inform clinical decision-making. However, as with any diagnostic tool, overuse may lead to unnecessary interventions and costs on the healthcare system. To better understand the use of ultrasonography in hospitalized kidney transplant recipients and outcomes of subsequent interventions, we conducted a single-center retrospective study at a large transplant program in Ontario, Canada. We noted that over 30% of admissions resulted in a ultrasonographic survey within the first 24 h of presentation; however, most of these did not change clinical management or lead to a subsequent procedural intervention. Using multivariable logistic regression, we identified predictors for receiving an ultrasound, including time from transplantation, elevated serum creatinine and infectious diagnosis. Procedural interventions (e.g., drain or biopsy) resulted from less than 20% of all ultrasound investigations, with patients closer to the time of index transplant or with elevated serum creatinine values more likely to receive an intervention. In conducting a cost analysis, we estimated that approximately $80 000 CAD per year could be saved with more selective decisions on ultrasound requisitions. Overall, our results indicate that despite being an informative tool, the broad use of ultrasonography in the kidney transplant population may not yield significant changes to transplant care.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".