Positive surgical margins during partial nephrectomy for renal cell carcinoma: Results from Canadian Kidney Cancer information system (CKCis) collaborative
Bibliographic record
Abstract
INTRODUCTION: We sought to determine the incidence, risk factors, and prognosis for patients with positive surgical margin (PSM) during partial nephrectomy (PN) for renal cell carcinoma (RCC). METHODS: From the Canadian Kidney Cancer information system (CKCis) database, a historical cohort of PN patients with PSM were identified and compared to negative surgical margin (NSM). Risk factors for PSM were examined through multivariable logistic regression. Kaplan-Meier curves were used to compare progression-free survival. RESULTS: Of 1103 patients, 972 (88.1%), 71 (6.4%), and 60 (5.4%) had NSM, PSM, and unknown status, respectively. Median patient age and tumour size were 61 years and 3.0 cm for both groups. From multivariable analysis, pathological stage ≥T3 (odds ratio [OR] 2.51; 95% confidence interval [CI] 1.13-5.60) and Fuhrman grade 4 (OR 5.35; 95% CI 1.11-25.72) were associated with PSM, whereas age, operative technique, and tumour size were not. Forty-nine (5.0%) patients from the NSM cohort and seven (9.9%) from the PSM cohort had a local/systemic progression of disease (adjusted hazard ratio [HR] 1.4; 95% CI 0.6-3.6). There were three (0.3%) cancer-related deaths in the NSM group and none in the PSM group. After median followup of 19 (interquartile range [IQR] 5-42) and 15 (IQR 7-30) months, 855 (91.4%) and 61 (89.7%) patients were alive in the NSM and PSM groups, respectively. CONCLUSIONS: PSM occurred in 6.4% of PNs performed for RCC in this pan-Canadian cohort. Higher stage and grade are associated with a higher risk of positive margin. The small association between a PSM and progression suggests that complete nephrectomy is not necessary in patients with a PSM. The main study limitations are lack of nephrometry score and possible reporting bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".