Postoperative outcomes of kidney stone surgery in patients with spinal cord injury: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Spinal cord injury (SCI) is associated with an increased risk of nephrolithiasis. We performed a systematic review and meta-analysis to assess stone clearance and complication rates following surgical treatment of kidney stones in this population. We systematically reviewed the Ovid MEDLINE, Embase, CENTRAL, and Web of Science databases for studies examining outcomes of kidney stone procedures in SCI patients. Our primary outcomes were stone-free rate (SFR) and complications, as categorized by Clavien-Dindo classification. A meta-analysis of comparative studies was performed to assess differences in outcomes between SCI and non-SCI patients following PCNL. A total of 27 retrospective and observational articles were included. Interventions for kidney stones included PCNL, shockwave lithotripsy (SWL), and ureteroscopy. Pooled SFR in SCI patients undergoing surgery for kidney stones was 54.1%, for SWL, 73.6% for PCNL, and 36.2% for ureteroscopy. Four studies compared outcomes following PCNL in SCI and non-SCI patients. Meta-analysis found that there were higher rate of grades I (OR 9.54; 95% CI, 3.06-29.79), II (OR 3.38; 95% CI, 1.85-6.18), and III-V (OR 2.38; 95% CI, 1.35-4.19) complications in SCI patients compared to non-SCI patients following PCNL. The rate of infectious complications was also higher in patients with SCI (OR 6.15; 95% CI, 1.86-20.39). However, there was no difference in SFR (OR 0.64; 95% CI, 0.15-2.64) between groups. Patients with SCI are at higher risk of complications following PCNL compared to non-SCI patients. SFR after PCNL was equivalent between groups, suggesting that PCNL is an effective surgery for kidney stones in SCI 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".