Are renal stone protocol computed tomography reports giving us enough information?
Bibliographic record
Abstract
INTRODUCTION: Non-contrast computed tomography (CT) is the gold-standard diagnostic test for urolithiasis. Little is published regarding which information needs to be included in the report for it to be most useful to the healthcare team for efficient triage and high-quality patient care. This study aimed to assess the quality and variability of CT scan reporting at a single Canadian tertiary academic medical center. METHODS: We completed a retrospective review of 100 consecutive renal colic CT scans. Descriptive statistics were used to report the frequency with which specific elements commonly used by urologists to triage and treat patients were included in radiology reports. RESULTS: Our sample had a mean age of 51.4±13.1 years. Stone size was universally reported for obstructing stones but was less frequently reported for non-obstructing stones (100% vs. 86.8%). A similar trend was observed for the exact stone number (100% vs. 93.4%). Non-obstructing stones were more likely than obstructing stones to be reported in one dimension (77.5% vs. 47%). Obstructing stones were reported in three dimensions 27% of the time. CT reports commonly include the presence or absence of hydronephrosis status (98%) but are less likely to include renal size (32%) and periureteral stranding (16%). Hounsfield units (HU) were reported in 3% of the reports, but skin-to-stone distance (SSD) and radiation dose were never reported. CONCLUSIONS: Reports routinely included assessments of stone size, location, and number (although not uniformly). HU, SSD, and radiation dose were rarely reported. This provides insight into opportunities for standardized reporting to optimize knowledge transfer that may result in clinical efficiency and improved quality of patient 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 | 0.041 | 0.318 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".