Prediction of calcium oxalate monohydrate stone composition during ureteroscopy
Bibliographic record
Abstract
Introduction: Prior research shows that Ho:YAG lithotripsy produces tiny dust fragments at low pulse energy (0.2J). However, calcium oxalate monohydrate (COM) stones may not fragment at this low pulse energy setting. Stone composition is rarely known until after surgery and historically, attempts to predict stone composition on the basis of endoscopic stone appearance were unsuccessful. Current endoscopic technology permits visual details that previously were not evident. As COM appears black under ambient light, we attempt to predict COM stone composition at the time of ureteroscopy based on its endoscopic appearance. Methods: Consecutive subjects undergoing ureteroscopy for stone disease were studied. Any portion of the stone that appeared black under endoscopic vision was considered clinical evidence of COM. Predicted stone composition was correlated with post-operative calculus analysis. Results: 46 consecutive ureteroscopic stone cases were analyzed prospectively. 25 of 28 subjects (89%) with black stones had stones later proven to be COM by composition analysis, versus one of 18 patients (6%) with non-black stones that were COM (p<0.0001). A black endoscopic stone appearance had a positive predictive value for COM of 89% and a non-black endoscopic stone appearance had a negative predictive value for COM of 94% (sensitivity 96%, specificity 83%). Conclusions: COM may reasonably be predicted intra-operatively by its black endoscopic appearance. The clinical utility would be to use higher laser pulse energy settings than for non-COM compositions. This data raises the possibility that more sophisticated optical characterization of endoscopic stone appearance may prove to be a useful tool to predict stone composition.
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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 | 0.003 |
| 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".