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Record W2588034690 · doi:10.1093/ndt/gfw148.05

TO031IMPACT OF RENAL DENERVATION (RDN) IN PATIENTS WITH LOIN PAIN HEMATURIA SYNDROME (LPHS): THE PRAIRIE LPHS STUDY

2016· article· en· W2588034690 on OpenAlexaffabout
Bhanu Prasad, Jennifer St.Onge, Kunal Goyal, Francisco J. Blanco

Bibliographic record

VenueNephrology Dialysis Transplantation · 2016
Typearticle
Languageen
FieldMedicine
TopicMuscle and Compartmental Disorders
Canadian institutionsCypress Health RegionRegina Qu'Appelle Health Region
Fundersnot available
KeywordsMedicineDenervationInternal medicineSurgery

Abstract

fetched live from OpenAlex

Introduction and Aims: Loin pain hematuria syndrome (LPHS) is a painful and incapacitating condition that typically afflicts young women. Treatment options are either opiates and/or surgical denervation of the renal nerves that includes auto transplantation or even nephrectomy with varying success. Methods: Three patients between the ages of 28-62 years (all female) with LPHS underwent endovascular ablation of the renal nerves between July and November 2015 using the Vessix ™renal denervation system. The number and frequency of pain medications, EQ-5D, McGill Pain Questionnaire, Geriatric Depression Score, Short Form (SF)- 36, and Oswestry Disability Index were measured at baseline and at 3 months post-procedure to evaluate changes in pain, disability, quality of life and mood. Results: There were significant improvements in pain (McGill Pain Questionnaire), disability (Oswestry disability index), and quality of life (EQ-5D and SF-36) from baseline to 3-months post-procedure. 2/3 patients were off tylenol #3 and non steroidal anti-inflammatory use, whilst the third had a 70% reduction in the dose of morphine.

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 score0.005
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.230
Teacher spread0.222 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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