Partial Nephrectomy A Comparison between Different Modalities
Bibliographic record
Abstract
Kidney cancer, with 4% of all malignancies, is one of the most common malignancies occurring among in adults. In Saudi Arabia, kidney cancer comprises 2.3% of all cancers, and its incidence has increased by 33%. Partial nephrectomy (PN) is considered as the gold standard for T1 renal masses.In this retrospective study, we did a chart review for all patients who underwent PNs between April 2013 and February 2019. Data comprised pre-sentation, tumor size, type of procedure (open vs. laparoscopic vs. robotic), and intra- and post-operative complications. Chi-square, ANOVA, and cross-tabulation were done using SPSS software. P > 0.05 was considered significant. Approval was obtained from the institutional review board of King Abdullah International Medical Research Center.In all, 69 patients were identified: 26 (37.7%) males and 43 (62.3%) females, with mean age = 54.53 ± 13.21 years; mean body mass index = 32.36 ± 7.03, and mean tumor size = 3.7 ± 1.72 cm. In terms of presentation, most patients (50, 72.4%) presented incidentally as opposed to symptomatic presentation. Of these patients, 18 (26.1%) underwent open partial nephrectomy (OPN), 29 (42%) laparoscopic partial nephrec-tomy (LPN), and 22 (31.9%) robotic partial nephrectomy (RPN). On comparing minimally invasive surgery (MIS) PN with OPN, we found that OPN had more blood loss and a longer hospital stay but a shorter operating room (OR) time.Results of PN irrespective of the procedure type, whether it was OPN, LPN, or RPN, were similar if performed by experienced surgeons. However, open procedures involved a higher blood loss, more operative time, and longer hospital stay when compared with minimally invasive techniques.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".