Unintended Consequences of Laparoscopic Surgery on Partial Nephrectomy for Kidney Cancer
Bibliographic record
Abstract
PURPOSE: Recent evidence suggests that partial nephrectomy may be associated with improved survival compared to radical nephrectomy for renal cell carcinoma but partial nephrectomy may be underused. We examined whether the introduction of laparoscopic radical nephrectomy contributed to low partial nephrectomy use with time. MATERIALS AND METHODS: We identified all patients treated surgically for renal cell carcinoma in Ontario, Canada between 1995 and 2004 using the Ontario Cancer Registry, a population based tumor registry. A multinomial logistic regression model was used to relate the relative numbers of patients with open and laparoscopic radical nephrectomy, and partial nephrectomy to patient age, gender and surgery year. The partial nephrectomy time trend was investigated by fitting a segmented regression model. RESULTS: Of 7,830 surgically treated patients 7,042 (89.9%) vs 788 (10.1%) underwent radical vs partial nephrectomy. Segmented regression showed a clear change in partial nephrectomy use with time (p = 0.001), such that the odds of partial nephrectomy increased by 18% per year before January 2003 (OR 1.18, 95% CI 1.14-1.23) and subsequently decreased by 12% per year (OR 0.88, 95% CI 0.75-1.02). In the multinomial regression model age and surgery year but not gender were independently associated with partial nephrectomy. CONCLUSIONS: Partial nephrectomy use for renal cell carcinoma remains low, particularly in elderly patients. The introduction of laparoscopic radical nephrectomy coincided with decreased uptake and use of partial nephrectomy for renal cell carcinoma. Although it was hypothesized previously, to our knowledge this is the first study to suggest that the introduction of laparoscopy in renal surgery has negatively impacted partial nephrectomy use.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".