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Diagnostic tests in urology: urine cytology

2012· article· en· W1500536114 on OpenAlexaff
Luke T. Lavallée, Dean Fergusson, Philipp Dahm, Charles D. Scales, Kelsey Witiuk, Rodney H. Breau

Bibliographic record

VenueBritish Journal of Urology · 2012
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsCystoscopyUrine cytologyCytologyUrineMedicineUrologyTriageUrothelial carcinomaUrinary systemBladder cancerInternal medicinePathologyCancerMedical emergency

Abstract

fetched live from OpenAlex

What's known on the subject? and What does the study add? Urine cytology is frequently used by urologists to evaluate patients with microscopic or gross haematuria. The results of urine cytology can be used as impetus to perform or triage further diagnostic studies, e.g. cystoscopy. The impact of urine cytology results on patient care warrants clarifying. This evidence-based medicine article explores how positive or negative urine cytology will impact the probability that a patient has urothelial carcinoma of the bladder before cystoscopy.

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)0.012
metaresearch head score (Gemma)0.047
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: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0050.005
Science and technology studies0.0010.004
Scholarly communication0.0050.005
Open science0.0020.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.015
GPT teacher head0.283
Teacher spread0.268 · 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
GenreReview

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

Citations5
Published2012
Admission routes1
Has abstractyes

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