Repeat Imaging to Avoid Surgery: An Initiative to Reduce-Negative Ureteroscopy in Patients with Ureteral Stones
Bibliographic record
Abstract
What's known on the subject? and What does the study add?Negative ureteroscopy is a clinical occurrence defined by ureteroscopy being performed for a ureteric or renal calculus identified on radiographic imaging pre-operatively, with no calculus ultimately being identified intra-operatively due to the calculus being passed prior to the procedure being performed.This occurrence has been reported in the existing literature with rates between 6.3 and 9.8% in cohorts consisting of patients with both ureteric and renal calculi.The rates of negative ureteroscopy can be reduced by having more timely operative intervention, and by utilizing repeat pre-operative imaging when indicated.Smaller and more distal calculi are more likely to pass prior to intervention and result in a negative ureteroscopy.This study provides further information regarding the rates of negative ureteroscopy in a specific cohort of patients with only ureteric calculi.These patients did have a higher rate of negative ureteroscopy than other populations in the literature.Additionally, patients who were more symptomatic from their stone undergoing more expedited intervention were actually more likely to have passed their stone prior to intervention when compared to patients undergoing delayed intervention.This highlights the need for repeat imaging when available prior to intervention.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".