Is computed tomography-defined obstruction a predictor of urological intervention in emergency department patients presenting with renal colic?
Bibliographic record
Abstract
Introduction: Our objective was to determine whether unilateral calculus-induced ureteric obstruction on computed tomography (CT) was independently associated with the need for urological intervention and 30-day return to the emergency department (ED).Methods: We performed a retrospective cohort study of patients with symptomatic urinary calculi diagnosed by unenhanced helical CT. Stepwise regression analysis was used to determine the predictors of urological intervention and 30-day return to the ED. Potential predictors assessed included: patient demographics, calculus size, calculus location, degree of obstruction, analgesic doses, signs and symptoms of infection, serum creatinine, cumulative intravenous fluid administered, and the prescription of medical expulsive therapy.Results: Of 195 patients, 81 (41.5%) underwent urological intervention. The size of the calculus, its location, and the cumulative opioid dose were all independent predictors for urological intervention. Every 1 mm increase in calculus size increased the likelihood of intervention 2.2 times (odds ratio [OR] 2.17; 95% confidence interval [CI] 1.67‒2.85). Proximal stones were 4.7 times more likely to require intervention than distal calculi (OR 0.21; 95% CI 0.09‒0.49). Every 10 mg increase in morphine was associated with a 30% increase in the odds of intervention (OR 1.30; 95% CI 1.07‒1.58). Degree of obstruction was not associated with the need for urological intervention. Finally, none of the variables were predictors for 30-day return to the ED.Conclusions: Although stone size, proximal location, and severe pain, as indicated by higher opioid doses, were associated with the need for intervention, the degree of obstruction did not influence the management of patients with CT-defined urinary calculi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".