Bibliographic record
Abstract
Introduction and Objective: Recent evidence suggests that renal function correlates with cardiac morbidity and overall survival.However, nephronsparing approaches such as partial nephrectomy (PN) may be infrequently used in the treatment of renal masses, particularly in the elderly.We examined population-based trends for renal cell carcinoma (RCC) management over a 10-year period. Materials and Methods:We identified 7830 patients treated surgically for RCC in the province of Ontario, Canada between 1995 and 2004 using the Ontario Cancer Registry, a population-based tumour registry.Demographic, treatment and vital status information was obtained for all patients.A multivariable logistic regression model was used to determine predictors of PN use.A survival analysis was used to estimate disease-specific and overall survival.Results: The mean age of patients was 60 years, of which 4826 (61.6%) were men.Of these, 7042 (90%) were treated with radical nephrectomy (RN) and 788 (10%) with PN.There was a significant decrease in PN use with increasing age; 11.8% of patients younger than 50, compared with only 4.9% of patients 80 and older (p < 0.0001).An increase in PN usage was observed from 1995 to 2002, however a plateau was noted with increasing laparoscopic RN use in recent years.Laparoscopic procedure codes were implemented in 2002, and thereafter an increasing proportion of procedures were performed using this approach; 48 cases in 2002 compared with 296 in 2004.On multivariable logistic regression, age (p < 0.0001) and year of surgery (p < 0.0001), but not gender, were independently associated with PN use.The unadjusted 5-year cancer-specific and overall survival estimates were 86.1% and 71.2%, respectively.Conclusion: Although previously hypothesized, this is the first evidence on a population level suggesting that laparoscopic RN may have impacted adversely on the uptake and application of PN to RCC.The infrequent use of nephron-sparing surgery is especially evident in the elderly, who may benefit the most from this approach.There is a need for further education of urologists about the importance of nephron-sparing surgery in RCC.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.696 | 0.472 |
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".