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Record W2793296002 · doi:10.5489/cuaj.5246

Improving patient journey and quality of care: Summary from the second Bladder Cancer Canada-Canadian Urological Association- Canadian Urologic Oncology Group (BCC-CUA-CUOG) bladder cancer quality of care consensus meeting

2018· article· en· W2793296002 on OpenAlexaffvenueabout
Wassim Kassouf, Armen Aprikian, Fred Saad, Rodney H. Breau, Girish S. Kulkarni, David Guttman, Ken Bagshaw, Jonathan I. Izawa, Libni Eapen, Adrian Fairey, Alan So, Scott North, Ricardo Rendon, Srikala S. Sridhar, Fadi Brimo, Peter Chung, Darrel Drachenberg, Yves Fradet, Niels-Erik Jacobsen, Christopher Morash, Bobby Shayegan, Geoffrey Gotto, A. Zlotta, Neil Fleshner, D. Robert Siemens, Peter C. Black

Bibliographic record

VenueCanadian Urological Association Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsQueen's UniversityMcMaster UniversityDalhousie UniversityUniversity of British ColumbiaWestern UniversityUniversity of ManitobaUniversity of CalgaryPrincess Margaret Cancer CentreMcGill University Health CentreUniversity of OttawaUniversité LavalUniversity of AlbertaUniversité de Montréal
Fundersnot available
KeywordsMedicineBladder cancerCancerFamily medicineQuality (philosophy)Patient careOncologyInternal medicineUrologyGynecologyNursing

Abstract

fetched live from OpenAlex

In the January-February 2016 issue of the Canadian Urological Association Journal, a multidisciplinary committee published a white paper entitled "Recommendations for the improvement of bladder cancer quality of care in Canada: A consensus document reviewed and endorsed by Bladder Cancer Canada (BCC), Canadian Urologic Oncology Group (CUOG), and Canadian Urological Association (CUA), December 2015". 1 The paper was produced in response to concerns regarding the variability in management and in outcomes of patients with bladder cancer throughout centres and geographical areas in Canada.The final paper contents were the CUAJ -Consensus Statement Kassouf et al Bladder cancer quality of care result of consensus deliberations during a two-day meeting that took place in late 2014.In November 2016, another multidisciplinary committee consisting largely of the same members convened the "2nd BCC-CUA-CUOG Bladder Cancer Quality of Care Meeting 2016".The focus was on patient journey and optimizing management.The following document is a summary of the proceedings of this meeting.The objectives for the meeting were the following: I Patient journey:• To discuss unmet needs in bladder cancer care from the patient perspective.II Optimizing management:• To select the top 10 indicators of bladder cancer quality of care and establish benchmarks; • To develop a score card for measurement of bladder cancer quality of care; • To address complex bladder cancer management from a training perspective;• To identify bladder cancer centres of expertise across Canada using refined criteria;• To discuss the establishment of a bladder cancer research network of excellence; I -Unmet needs in bladder cancer: The patient perspective Patient representatives from Bladder Cancer Canada (BCC) presented their perspective on unmet needs in bladder cancer care.These perspectives were gathered by patients from Bladder Cancer Canada through the BCC website / discussion forum, BCC patient-to-patient emails and phone calls, as well as a patient needs survey conducted at the Princess Margaret Hospital in Toronto.Patient needs were subdivided into four timeframes across the patient journey: 1) beginning the journey with signs/symptoms (pre-diagnosis); 2) from diagnosis to treatment; 3) during treatment; and, 4) after treatment: living "the new normal".

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)0.013
metaresearch head score (Gemma)0.027
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.543

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.288
Teacher spread0.262 · 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
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

Citations10
Published2018
Admission routes3
Has abstractyes

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