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Record W2416207901

A prospective randomized trial comparing lidocaine and lubricating gel on pain level in patients undergoing transrectal ultrasound prostate biopsy.

2002· article· en· W2416207901 on OpenAlexaff
Fred Saad, Robert Sabbagh, Michael McCormack, François Péloquin

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineLidocaineVisual analogue scaleProstate biopsySedationBiopsyProstateSurgeryRandomized controlled trialProspective cohort studyAnesthesiaRadiologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: To compare patient reported pain during TRUS guided biopsies using intrarectal lidocaine gel versus lubricating gel. MATERIALS AND METHODS: From May 2000 to May 2001, 360 men undergoing transrectal prostate biopsy were enrolled in this study. Patients were randomized into two groups. In group 1, 180 patients received 10 cc of 2% intrarectal lidocaine gel (Xylocaine 2% jelly, Astra Pharma Inc.) 5 to 10 minutes before the procedure and in group 2, 180 patients received 10 cc of lubricating gel. No other sedation or analgesia was given. Pain level immediately after the last biopsy was assessed using a 10-point linear visual analog pain scale. RESULTS: The median pain score during transrectal prostate biopsy was 2 (range 0 to 8) and 3 (range 1 to 10) in groups 1 and 2, respectively (p = 0.0001). Only minor complications occurred and complication rates were not significantly different between the groups. CONCLUSION: Rectal administration of lidocaine gel is safe, simple and effective for reducing the pain level associated with transrectal prostate biopsy.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.036
GPT teacher head0.234
Teacher spread0.199 · 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 designRandomized trial
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

Citations32
Published2002
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicProstate Cancer Diagnosis and Treatment→French-language works237,207→