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Record W2570494786 · doi:10.1016/j.urolonc.2016.12.003

Quality indicators in the management of bladder cancer: A modified Delphi study

2017· article· en· W2570494786 on OpenAlexafffund
Satya Rashi Khare, Armen Aprikian, Peter C. Black, Normand Blais, Chris Booth, Fadi Brimo, Joseph L. Chin, Peter Chung, Darrel Drachenberg, Libni Eapen, Adrian Fairey, Neil Fleshner, Yves Fradet, Geoffrey Gotto, Jonathan I. Izawa, Michael A.S. Jewett, Girish S. Kulkarni, Louis Lacombe, Ronald B. Moore, Christopher Morash, Scott North, Ricardo Rendon, Fred Saad, Bobby Shayegan, Robert Siemens, Alan So, Srikala S. Sridhar, Samer L. Traboulsi, Wassim Kassouf

Bibliographic record

VenueUrologic Oncology Seminars and Original Investigations · 2017
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsMcMaster UniversityDalhousie UniversityUniversité LavalUniversity of AlbertaMcGill UniversityMcGill University Health CentreUniversity of OttawaUniversity of ManitobaUniversity of CalgaryPrincess Margaret Cancer CentreWestern UniversityUniversity of TorontoUniversity Health NetworkUniversité de MontréalQueen's UniversityUniversity of British Columbia
FundersBladder Cancer Canada
KeywordsBenchmarkingMedicineDelphi methodBladder cancerDelphiQuality managementBest practiceQuality (philosophy)Medical physicsOperations managementCancerManagement systemComputer scienceArtificial intelligenceManagement

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.085
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.450

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.116
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.410
Teacher spread0.329 · 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 designQualitative
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

Citations24
Published2017
Admission routes2
Has abstractno

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