Bibliographic record
Abstract
You have accessJournal of UrologyReview Article1 Sep 2021Editorial CommentThis article comments on the following:Natural History of Post-Treatment Kidney Stone Fragments: A Systematic Review and Meta-Analysisis a letter which has replyReply by Authors Anthony S. Emmott, and Ben H. Chew Anthony S. EmmottAnthony S. Emmott Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada More articles by this author , and Ben H. ChewBen H. Chew Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001836.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail We applaud Brain et al for performing the first systematic review and meta-analysis of the natural history of residual asymptomatic kidney stone fragments after treatment with shock wave lithotripsy, ureteroscopy or percutaneous nephrolithotomy. In total they included 2,096 patients from 18 studies. Significant heterogeneity existed between studies with regard to definitions of stone-free status, disease progression and imaging modality utilized for diagnosis of residual fragments. Overall, re-intervention rates for residual fragments increased with time. However, there was no significant difference in intervention rates or disease progression rates between residual fragments over 4 mm compared with fragments 4 mm or less. These findings call into question the term “clinically insignificant residual fragments.” This term should no longer be used to describe stone fragments identified after intervention, as these residual fragments may act as a nidus for stone growth. The goal of treatment for kidney stones should therefore be to obtain a stone-free status and minimize repeat interventions, particularly considering we do not fully understand the impact of repeat interventions on patients’ quality of life. There is a wide range of stone-free rates quoted in the literature for stone removal procedures. The imaging modality and timing of re-imaging are important determinants of stone-free status, with studies showing that computerized tomography is more sensitive for residual stone fragments than other modalities, thus resulting in lower stone-free rates.1 Re-imaging following stone removal procedures is important to identify patients with residual stone fragments who may be at risk for disease progression or re-intervention. Despite the high prevalence of kidney stones, there is very limited literature on the natural history of residual fragments with a near absence of prospective trials, emphasizing the need for further investigation. Future studies to understand the impact of repeat interventions on patient quality of life would also be of benefit. Reference 1. : Computed tomography-determined stone-free rates for ureteroscopy of upper-tract stones. J Endourol 2009; 23: 379. Google Scholar © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesJournal of UrologyApr 27, 2021, 12:00:00 AMNatural History of Post-Treatment Kidney Stone Fragments: A Systematic Review and Meta-AnalysisJournal of UrologyJun 16, 2021, 12:00:00 AMReply by Authors Volume 206Issue 3September 2021Page: 536-537 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Anthony S. Emmott Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada More articles by this author Ben H. Chew Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada More articles by this author Expand All Advertisement Advertisement PDF DownloadLoading ...
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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 | 0.007 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.271 | 0.150 |
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".