Natural History of Post-Treatment Kidney Stone Fragments: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: We assessed the literature around post-treatment asymptomatic residual stone fragments and performed a meta-analysis. The main outcomes were intervention rate and disease progression. MATERIALS AND METHODS: We searched Ovid®, MEDLINE®, Embase™, the Cochrane Library and ClinicalTrials.gov using search terms: "asymptomatic", "nephrolithiasis", "ESWL", "PCNL", "URS" and "intervention." Inclusion criteria were all studies with residual renal fragments following treatment (shock wave lithotripsy, ureteroscopy or percutaneous nephrolithotomy). Analysis was performed using 'metafor' in R and bias determined using Newcastle-Ottawa scale. RESULTS: =0.57, Cochran's Q=7.11 (p=0.07) and Egger's regression: z=-0.75, p=0.45. Bias analysis demonstrated a moderate risk. CONCLUSIONS: Larger post-treatment residual fragments are significantly more likely to require further intervention especially in the long term. Smaller fragments, although less likely to require further intervention, still carry that risk. Notably, there is no significant difference in disease progression between fragment sizes. Patients with residual fragments should be appropriately counselled and informed decision-making regarding further management should be done.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".