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Variation in radiation oncology referral and adjuvant radiotherapy use following prostatectomy: A population-based study of health system performance.

2017· article· en· W2891344194 on OpenAlexaffabout
Chunzi Jenny Jin, Timothy P. Hanna, E. Francis Cook, Qun Miao, Michael Brundage

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineProstatectomyProstate cancerReferralInternal medicineCohortPopulationRadiation therapyCancer registryCancerSurgeryOncologyFamily medicine

Abstract

fetched live from OpenAlex

e16551 Background: Evidence-based guidelines confirm a survival advantage of adjuvant radiotherapy (ART) for prostatectomy (RP) patients with high-risk pathology. The efficacy of deferred salvage RT is under evaluation as an alternative strategy, and guidelines recommend radiation oncology (RO) referral for discussion of options. We report RO referral patterns, ART use, and factors associated with these patterns in a contemporary population-based RP cohort. Methods: Electronic treatment records were linked to Ontario's cancer registry. Multivariable regression was used to evaluate clinical and health systems factors associated with RO referral and ART use ≤ 6 months post-RP. Results: From January to November 2012, 2,663 prostate cancer patients received RP in Ontario. Among 1,261 with adverse pathology, 492 (39%) were referred to RO ≤ 6 months post-RP, of which 51% received ART. Multivariable analysis demonstrated that RO referral was more frequent for cases of T3b/T4 disease [OR 17.87; p < 0.0001], T3a disease [OR 5.24; p < 0.0001], Gleason score 8-10 disease [OR 11.32; p < 0.0001], Gleason score 7 disease [OR 4.18; p < 0.0001], at least one non-apex margin positive [OR 4.20; p < 0.0001], an apex only positive margin [OR 2.60; p < 0.0001], RO referral prior to RP [OR 1.95; p < 0.0001], low RP volume hospitals [OR 2.50; p < 0.0001], and increased distance of patient residence from cancer center [OR 1.73; p = 0.01]. There was wide geographic variation in RO referral rates (range 6%-66%; p < 0.0001). Among patients seen by RO, only T3b/T4 disease [OR 5.37; p < 0.0001], T3a disease [OR 2.72; p < 0.0001], at least one non-apex positive margin [OR 2.81; p < 0.0001], and an apex only positive margin [OR 1.32; p < 0.0001] remained predictive of ART on multivariable analysis. Conclusions: Nonmedical factors are important determinants of whether patients are referred for discussion of ART post-RP. Post-RO consultation, treatment decisions are correlated with pathologic findings. Large inter-center variations persist in referral and treatment post-RP, suggesting that further understanding of the reasons for variation could improve access to potentially curative RT in this setting.

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)0.004
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 score0.502
Threshold uncertainty score0.998

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.119
GPT teacher head0.526
Teacher spread0.407 · 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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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