<p>Temporal Trends in Selecting Patients for Partial Nephrectomy for Small Renal Cell Carcinomas in Alberta, Canada</p>
Bibliographic record
Abstract
BACKGROUND: When technically feasible, partial nephrectomy (pN) is preferred over radical nephrectomy (rN) due to similar oncological control with preservation of renal function. Here, we evaluate the incorporation of pN into practice for small renal masses and examine the associated outcomes. METHODS: We included patients who had undergone either a partial or radical nephrectomy in Alberta, Canada for renal cell carcinomas with pathology tumor stage T1a between 2002 and 2014 (N=1449). Patients were excluded if they had multiple tumors or if they were on dialysis prior to nephrectomy. RESULTS: pN use increased over the duration of the study period. Patients treated after the introduction of guidelines (2007) recommending the use of pN were significantly more likely to receive a pN (OR: 2.709, 95% CI: 1.944-3.775; p<0.001) after adjusting for baseline estimated glomerular filtration rate (GFR), age, and sex. Patients who received rN were at significantly increased risk of death (HR: 1.528, 95% CI: 1.029-2.270; p=0.036) after controlling for baseline GFR, age, and sex. Baseline GFR significantly affected odds of receiving pN (p<0.050) in the entire cohort, but subgroup analysis of more recently diagnosed patients (2011-2014) showed that only patients with kidney failure (GFR <15) were less likely to have received pN. DISCUSSION: The utilization of pN for patients with pT1a renal cell carcinoma has increased significantly over time and has been accelerated by the introduction of guideline recommendations. Patients treated with pN over the study period had superior overall survival.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".