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Record W2153204683 · doi:10.4111/kju.2012.53.3.154

Effects of Partial Nephrectomy on Postoperative Blood Pressure

2012· article· en· W2153204683 on OpenAlexaff
Nathan Lawrentschuk, Greg Trottier, Karli Mayo, Ricardo Rendon

Bibliographic record

VenueKorean journal of urology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsDalhousie UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineBlood pressureDiastoleNephrectomySurgeryAnesthesiaInternal medicineKidney

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.250
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2012
Admission routes1
Has abstractyes

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