Costovertebral angle as a novel tool for predicting the thoracic complication risk following percutaneous nephrolithotomy requiring supracostal access
Bibliographic record
Abstract
Introduction: The objective of this study was to determine whether the costovertebral angle (CVA) and other factors can predict the risk of thoracic complications following percutaneous nephrolithotomy (PCNL). Methods: The data of patients who underwent prone PCNL with supracostal access at Suleyman Demirel University Hospital between January 2015 and December 2019 were retrospectively reviewed. Patients’ demographics information (age, sex, body mass index [BMI], stone size, and stone location), operative data (supracostal access site, renal puncture site, and laterality), and postoperative thoracic complications (pleural injury) were evaluated. The CVA was measured on preoperative posteroanterior chest X-ray images. The mean CVA of patients with and without thoracic complications was evaluated. Results: A total of 89 patients (mean age 46.12±15.66 years; 59 men and 30 women) with supracostal access were included in the study. Thoracic complications occurred in 17 (19.1%) patients. Nine (52.9%) hemothorax cases, five (29.4%) pneumothorax cases, and three (17.7%) urinothorax cases were detected. There was a statistically significant difference in the complication rate compared to the percutaneous access site (10th–11th supracostal vs. 11th–12th supracostal) (p=0.004). The mean CVA was significantly lower in patients with complications (45.47±3.59) than in those without complications (53.26±5.98) (p=0.000). No association was found (p>0.05) with age, sex, BMI, laterality, stone surface area, and access site among patients with and without thoracic complications. Conclusions: Preoperative CVA can be an effective tool in predicting the risk of postoperative thoracic complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".