Does Hounsfield Unit have any significance in predicting intra and postoperative outcomes in retrograde intrarenal surgery using holmium and Thulium fiber laser? A critical analysis of results from the FLEXible ureteroscopy Outcomes Registry (FLEXOR)
Bibliographic record
Abstract
Abstract The objective was to evaluate outcomes of retrograde renal surgery for intrarenal stones of any size, number, and position, comparing hard versus soft stones based on their attenuation on computed tomography (Hounsfield Unit-HU). Exclusion criteria; children/adolescents, ureteric stones, renal anomalies, or bilateral surgery. Patients were divided into two groups according to the type of laser employed, i.e. Holmium:YAG (HL) and Thulium fiber laser (TFL). Residual fragments (RF) were defined as > 2 mm. Multivariable logistic regression analysis was performed to evaluate factors associated with RF and RF needing further intervention. 4208 patients from 20 centers were included. 3070 patients were operated on with HL. In HU < 1000 stones, the TFL group had larger stones (11.56 ± 10.38 vs 9.98 ± 6.89 mm,p < 0.001). Multiple and lower pole stones were more prevalent in the HL group. Lasing time was shorter in the TFL group (15.34 ± 12.55 vs 23.32 ± 15.66 minutes,p < 0.001). HL group had a higher incidence of RF (29.1% vs 13.7%,p < 0.001). Age, stone size, and HL were predictors of RF, whereas multiple stones, and HL of RF requiring retreatment. In HU ≥ 1000 stones, stone size was similar between the two groups, whilst multiple and lower pole stones were more prevalent in the HL group. Surgical time was significantly shorter in the HL group (64.48 ± 33.92 vs 79.54 ± 88.56,p < 0.001). Significantly higher incidence of RF was noted in HL (22.7% vs 9.8%,p < 0.001), whilst reintervention rate was significantly higher TFL group (69.6% vs 43.8%,p < 0.001). Age, stone size, and use of HL were predictors of RF, whilst recurrent stone formers, multiple stones, and use of TFL of RF requiring retreatment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".