The usefulness of stone density and patient stoutness in predicting extracorporeal shock wave efficiency: Results in a North African ethnic group
Bibliographic record
Abstract
INTRODUCTION: We determine the role of stone density and skin-to-stone distance (SSD) by non-contrast computed tomography of the kidneys, ureters and bladder (CT-KUB) in predicting the success of extracorporeal shock wave lithotripsy (ESWL). METHODS: We evaluated 89 patients who received ESWL for renal and upper ureteric calculi measuring 5 to 20 mm, over a 12-month period. The mean stone density in Hounsfield units (HU) and mean SSD in mm was determined on pre-treatment CT-KUB at the CT workstation. ESWL was successful if post-treatment residual stone fragments were ≤3 mm. RESULTS: ESWL success was observed in 68.5% of patients. Mean stone densities were 505 ± 153 and 803 ± 93 HU in the ESWL successful and failure groups, respectively (p < 0.001, student's t-test). The mean SSD were 10.6 ± 2.0 and 11.2 ± 2.6 cm in ESWL successful and failure groups, respectively; this was not statistically significant. CONCLUSIONS: This study shows that stone density can help to predict the outcome of ESWL. We propose that stone density <500 HU are highly likely to result in successful ESWL. Conversely, stone densities >800 HU are less likely to be successful.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".