The impact of a multidisciplinary small renal mass clinic on patient treatment decisions
Bibliographic record
Abstract
INTRODUCTION: Small renal masses (SRMs) are managed with active surveillance (AS), thermal ablation (TA), irreversible electroporation (IRE), or surgery, depending on patient and tumor factors. A novel SRM multidisciplinary clinic (SRMC), involving urologists and interventional radiologists, was established to provide patients with information on treatments options. The objective of this study was to evaluate the impact of the SRMC on treatment decision-making METHODS: Demographics, tumor characteristics, and treatment decisions were prospectively collected on patients (n=216) attending the SRMC between 2016 and 2019. A retrospective historic cohort (n=238) seen by urologists was used as a control group. Key variables were analyzed and compared. Patient satisfaction (n=27) was surveyed and responses were summarized and explored. RESULTS: Mean age, tumor size, and pathology was similar between groups; however, the SRMC cohort had more male patients (65.7% vs. 53.8%, p=0.009). Chosen treatment modality differed significantly between cohorts (p<0.0001). Patients in the historic cohort were treated by AS (41.5%), surgery (37.9%), TA (11.9%), watchful waiting (7.9%), and IRE (0.8%). SRMC patients were treated by TA (42.2%), AS (26.7%), surgery (21.3%), IRE (7.6%), and watchful waiting (2.2%). Post-hoc analysis revealed statistically significant differences in proportions of AS, TA, IRE, and surgery between cohorts. Patients reported high satisfaction with the collaborative approach. CONCLUSIONS: A multidisciplinary approach may have an impact on patient treatment decision-making for SRMs. Consultations involving a urologist and an interventional radiologist resulted in more TA and IRE and less AS and surgery. Future studies should evaluate if these findings occur in other centers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".