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Radiation oncologist consultations prior to prostatectomy in Ontario, Canada: Disparities and opportunities.

2021· article· en· W3169329629 on OpenAlexaffabout
Mark T. Corkum, Andrew Loblaw, Gerard Morton, Alexander V. Louie, Rachel Glicksman, Joseph L. Chin, Girish S. Kulkarni, Robert Dinniwell, Barbara J. Fisher, Refik Saskin, Jason Pantarotto, Andrew Warner, George Rodrigues

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsInstitute for Clinical Evaluative SciencesOttawa HospitalPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of TorontoLondon Health Sciences CentreHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineProstatectomyProstate cancerRadiation oncologistLogistic regressionPopulationOdds ratioCancer registryInternal medicineCancerGynecologyRadiation therapyEnvironmental health

Abstract

fetched live from OpenAlex

e17052 Background: Men with localized prostate cancer have many options for initial definitive treatment. In 2015, Cancer Care Ontario Quality Based Procedures (QBP) recommended that men undergoing radical prostatectomy (RP) in Ontario be seen by a radiation oncologist (RO) or discussed at a multidisciplinary case conference (MCC) prior to surgery. An a-priori target rate of 76% was set by QBP, but to our knowledge, has not been reported upon to date. Our objective was to use population-based data to explore factors associated with not receiving RO consult/MCC prior to RP. Methods: Men with localized prostate cancer diagnosed and treated in Ontario, Canada with RP between 2007 and 2017 were identified using administrative data from the Institute for Clinical Evaluative Sciences. Physician billing data was utilized to identify patients who received RO consult/MCC prior to RP. Trends were evaluated using the Cochran-Armitage test. Multivariable logistic regression was used to identify patient and provider factors predictive of RO/MCC prior to RP. Results: 31,467 men with localized prostate cancer underwent RP between 2007 and 2017. Prior to RP, 29.3% of men were seen by RO, 1.0% underwent MCC, and 1.6% had both. RO consult/MCC prior to RP increased from 18.0% in 2007 to 47.8% in 2017 ( p<0.001). On multivariable analysis, the Odds Ratio (OR) of RO consult/MCC prior to RP between the lowest and highest geographic regions (LHINs) was 8.79 (95% CI 6.83–11.32, p<0.001). RO consult/MCC was less likely to occur for patients living further from the nearest cancer center (OR 0.74 per 50km, 95% CI 0.70–0.77, p<0.001) and more likely to occur for men residing in the highest versus lowest income quintile regions (OR 1.42, 95% CI 1.30–1.55, p<0.001). Men with NCCN Low (OR 1.31, 95% CI 1.16–1.47, p<0.001), High (OR 1.20, 95% CI 1.09–1.31, p<0.001) or Very High (OR 1.24, 95% CI 1.11–1.30, p<0.001) risk disease were more likely to receive RO consult/MCC compared to those with favourable-intermediate risk disease. Of the 128 urologists who performed at least 10 RP between 2016 and 2017, RO referral/MCC rate ranged from 0% to 100%, with 31 urologists (24.2%) having ≥76% of their patients seen prior to RP. To meet QBP targets in 2017, an additional 701 men would have needed RO consult/MCC. If all were seen by RO, approximately 2.4 additional full time equivalent RO positions would be needed. Conclusions: Despite increasing rates of utilization, a large proportion of men are not seen by RO or MCC prior to RP in Ontario, Canada. While the largest factors predicting RO consult/MCC discussion appear to be geographic and which urologist performs the RP, these factors are closely intertwined. In addition, these factors may be related to RO availability and radiation system capacity, which would need to be addressed to meet patient demand should QBP consultation rates be mandated to reduce disparities in pre-RP consultation practices.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.028
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.436
GPT teacher head0.542
Teacher spread0.106 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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