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Record W2796147937 · doi:10.1016/j.juro.2018.02.1731

PD36-08 LOW RELIABILITY OF URODYNAMIC DIAGNOSIS OF DETRUSOR SPHINCTER DYSSYNERGIA

2018· article· en· W2796147937 on OpenAlexfundno aff
Brandi Miller, Chris Tallman, Michael Randazzo, Timothy B. Boone, Rose Khavari

Bibliographic record

VenueThe Journal of Urology · 2018
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsnot available
FundersSwiss Continence FoundationRick Hansen FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungContinence Foundation of AustraliaMichael Smith Health Research BCNational Science Foundation
KeywordsDyssynergiaMedicineUrethral sphincterDetrusor sphincter dyssynergiaUrinationUrologyInter-rater reliabilitySphincterUrinary systemUrethraSurgeryInternal medicineNeurological disorderPsychologyCentral nervous system disease

Abstract

fetched live from OpenAlex

CONCLUSIONS: In SCI patients with NLUTD, there is a high incidence of AD, which is independently predicted by SCI level and presence of NDO.Considering the risks associated with AD, we strongly recommend continuous cardiovascular monitoring during urodynamics in SCI patients and appropriate NDO treatment to reduce the probability of potentially life-threating complications.

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)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.007
GPT teacher head0.256
Teacher spread0.248 · 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 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
Published2018
Admission routes1
Has abstractyes

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