Standardized Reporting on the Preoperative CT Assessment of Potential Living Renal Transplant Donors: Can We Create a Universal Report Standard to Meet the Needs of Transplant Urologists?
Bibliographic record
Abstract
Purpose: Determine whether standardized template reporting for the preoperative assessment of potential living renal transplant donors improves the comprehensiveness of radiology reports to meet the needs of urologists performing renal transplants. Methods: Urologist and radiologist stakeholders from renal transplant centers in our province ratified a standardized reporting template for evaluation of potential renal donors. Three centers (A, B, and C) were designated “intervention” groups. Center D was the control group, given employment of a site-specific standardized template prior to study commencement. Up to 100 consecutive CT scan reports per center, pre- and post-implementation of standardized reporting, were evaluated for reporting specific outcome measures. Results: At baseline, all intervention groups demonstrated poor reporting of urologist-desired outcome measures. Center A discussed 5/13 variables (38%), Center B discussed 6/13 variables (46%), and Center C only discussed 1/13 variables (8%) with ≥90% reliability. The control group exhibited consistent reporting, with 11/13 variables (85%) reported at ≥90% reliability. All institutions in the intervention group exhibited excellent compliance to structured reporting post-template implementation (Centers A = 95%, B = 100%, and C = 77%, respectively). Additionally, all intervention centers demonstrated a significant improvement in the comprehensiveness of reports post-template implementation, with statistically significant increases in the reporting of all variables under-reported at baseline (P > .01). Conclusion: Standardized templates across our province for CT scans of potential renal donors promote completeness of reports. Radiologists can reliably provide our surgical colleagues with needed preoperative anatomy and incidental findings, helping to determine suitable transplant donors and reduce potential complications associated with organ retrieval.
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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 | 0.150 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".