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Record W1605307168 · doi:10.5772/27715

Percutaneous Renal Biopsy

2012· book-chapter· en· W1605307168 on OpenAlexaff
Louis‐Philippe Laurin, Alain Bonnardeaux, Michel Dubé, Martine Leblanc

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicinePercutaneousRenal biopsyNephrologyBiopsyRenal stoneRadiologySurgeryPercutaneous biopsyGeneral surgeryInternal medicineUrinary system

Abstract

fetched live from OpenAlex

Renal biopsy is an essential tool for the diagnosis of several renal diseases. The first renal biopsies were performed in 1923 as open surgical procedures (Gwyn, 1923). The percutaneous approach has been part of the clinical practice since 1951 (Iversen & Brun, 1951). A few years later, a description of the procedure with the patient placed in a prone position was published (Kark & Muehrcke, 1954). The new technique had a success rate of more than 90% and was not associated with major complications. Percutaneous ultrasoundguided renal biopsy is now considered as the standard method. It is a routine procedure that can be done with minimal discomfort to the patient. Here we review major topics related to this frequent and useful procedure in the field of nephrology.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.014

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 head0.020
GPT teacher head0.258
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2012
Admission routes1
Has abstractyes

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