Urine Glycosaminoglycan Scores for Surveillance of Recurrence in Intermediate- and High-risk Nonmetastatic Clear Cell Renal Cell Carcinoma—An Observational Prospective Multicentre Diagnostic Test Cohort Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Nonmetastatic (M0) clear cell renal cell carcinoma (ccRCC) recurs in ∼20% of patients within 5 yr after surgery. With no biomarkers available, recurrence detection relies on radiological imaging. Urine glycosaminoglycan profiles (GAGomes) were previously associated with M0 ccRCC recurrence. We conducted an observational prospective multicentre diagnostic test cohort study to evaluate GAGomes for postsurgery recurrence detection in M0 ccRCC. METHODS: Postsurgical M0 ccRCC patients with a Leibovich score of ≥5 points were included. Follow-up imaging up to 18 mo assessed radiological recurrence (reference standard). Urine GAGomes were measured every 3 mo to compute a GAGome score (index test). Sensitivity and specificity to radiological recurrence were calculated. The lead time between the first positive GAGome score and radiological recurrence was estimated. Bayesian joint modelling estimated recurrence-free survival hazard ratio (HR). KEY FINDINGS AND LIMITATIONS: Of the 393 patients screened (January 2020 to November 2021), 134 met the inclusion criteria. The median follow-up was 16 mo (interquartile range [IQR]: 12-18) for those without recurrence. At the last follow-up visit, 16% had recurred. The GAGome score had 90% sensitivity (95% confidence interval [CI]: 62-100%) and 51% specificity (95% CI: 30-71%) to radiological recurrence. The positive and negative predictive values were 26% (95%CI: 4-46%) and 97% (95% CI: 87-100%), respectively. The median lead time was 4.2 mo (IQR: 1.6-6.4). A 10-point GAGome score increase was associated with an HR of 1.62 (95% high density interval: 1.11-2.30) for recurrence. The main limitation was short follow-up time. CONCLUSIONS AND CLINICAL IMPLICATIONS: GAGome score had very high sensitivity to ccRCC recurrence, resulting in a negative predictive value of 97%. External validation foreseen in the study design aims to confirm its utility to personalise follow-up for M0 ccRCC patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".