Effects of Partial Nephrectomy on Postoperative Blood Pressure
Bibliographic record
Abstract
PURPOSE: The effects of partial nephrectomy (PN) on postoperative blood pressure (BP) are not known, and PN has the potential to worsen BP. We therefore sought to determine whether PN alters postoperative BP. MATERIALS AND METHODS: Patients who underwent PN for suspected malignancy at our institution from 2002 to 2008 were included. Data on BP and medication from before and after PN were retrieved from family physicians. BP and number of antihypertensive medications were compared after surgery with preoperative values by use of paired t tests and Chi-squared analyses, respectively. RESULTS: Of 74 patients undergoing PN and providing consent, 48 met the inclusion and exclusion criteria, with a median follow-up of 24 months. For the early postoperative period (1 month to 1 year after surgery), the mean BPs (132.3/77.0 mmHg) were unchanged compared with preoperative values (132.4/78.0 mmHg; p=0.59 systolic BP and p=0.30 diastolic BP). For the later postoperative period (beyond 1 year after surgery), the mean postoperative systolic BP was unchanged from the mean preoperative systolic BP (131.2 mmHg vs. 132.4 mmHg, respectively; p>0.30). However, the corresponding average diastolic BP was lower in the long term (78.0 mmHg versus 76.4 mmHg respectively; p=0.01). No significant difference in the mean number of BP medications prescribed preoperatively, at one year, and beyond one year was identified (p>0.37). CONCLUSIONS: PN does not result in initial or long-term postoperative deterioration in BP.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".