Does replacing intravenous pyelography with noncontrast helical computed tomography benefit patients with suspected acute urolithiasis?
Bibliographic record
Abstract
OBJECTIVE: To determine if replacing intravenous pyelography with noncontrast helical computed tomography (NHCT) scanning of the abdomen for the investigation of suspected acute urolithiasis results in shorter stays in the Emergency Department, reduced hospital admissions or fewer interventions. METHODS: A retrospective review of the charts of all patients who were discharged from the Emergency Department with a diagnosis of acute urolithiasis or renal colic was conducted. Length of stay, hospital admissions and the number of therapeutic interventions were compared for the 5-month period before and the 5-month period after the implementation of NHCT scanning of the abdomen as the primary investigation of suspected acute urolithiasis. RESULTS: Of 230 cases reviewed, 119 met all of the inclusion criteria (61 in the intravenous pyelography group and 58 in the NHCT group). No significant differences were found between the 2 groups on median length of stay in the Emergency Department (7.6 h v. 6.2 h), hospital admission rates or post-test therapeutic interventions. CONCLUSIONS: Replacing intravenous pyelography with NHCT scanning for the investigation of suspected acute urolithiasis does not result in significantly shorter stays, reduced hospital admissions or fewer interventions for patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".