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Record W7095809773

CUAJ • July-August 2013 • Volume 7, Issues 7-8 © 2013 Canadian Urological Association

2013· article· en· W7095809773 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProstate cancerReferralCancerWatchful waitingMedical recordRadiation therapyCancer registryMultidisciplinary approachChart
DOInot available

Abstract

fetched live from OpenAlex

Background: Wait times in cancer diagnosis and treatment may significantly affect a patient’s treatment outcome, prognosis and quality of life. The purpose of this study was to capture wait time intervals for patients with prostate cancer treated with radiotherapy (RT) at the Odette Cancer Centre, Toronto, Ontario, Canada and to compare patients diagnosed in a rapid diagnostic unit (RDU) versus the usual community referral process. Methods: Patients agreed to participate in the study during their RT planning sessions. A semi-structured interview and chart abstrac-tion was conducted to record key wait time milestones. Results: A total of 87 patients participated in the study: 44 RDU patients and 43 community patients. The median overall wait time intervals from suspicion of prostate cancer to RT was 138 and 183 days, respectively (p = 0.046). There were statistically significant differences observed for other key wait time intervals favouring the RDU cohort: suspicion to decision-to-treat (DTT; p = 0.012), urologist visit to diagnosis (p = 0.0094), diagnosis to DTT (p = 0.018), and diagnosis to treatment (p = 0.016). Risk category and Gleason sum was independently predictive of longer intervals from diagnosis to DTT. Interpretation: Wait time intervals from suspicion to treatment are significantly shorter for prostate cancer patients in 2011 to 2012 than in 2003 when patients were diagnosed and referred in the community setting. A prostate-specific RDU further reduced a num-ber of key wait time intervals supporting more multidisciplinary RDUs for common diseases. Further work needs to be done to identify why delays are occurring and to develop new processes to minimize delays.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1270.058

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 head0.012
GPT teacher head0.191
Teacher spread0.179 · 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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

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