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Record W2514130550 · doi:10.1016/s0167-8140(16)33454-5

55: Surveying the Landscape: Congruence of a Provincial Cancer Agency Patient Education Program with National Standards

2016· article· en· W2514130550 on OpenAlexaff
Paris‐Ann Ingledew, Joy Bunsko, Angela C. Bedard, Pamela Dent, Lynne Ferrier, Anne Hughes, Brenda Ross, Amanda Bolderston

Bibliographic record

VenueRadiotherapy and Oncology · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsCongruence (geometry)Agency (philosophy)International agencyMedicineSociologyPsychologyCancerSocial scienceInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

was a doubling in ART rates amongst all RP cases, ranging from 5.4% in 2003-2004 to 11.0% in 2011-2012 (p < 0.001), compared to relatively stable SRT rates of 8.5% ± 0.2% (7.9% in 2003-2004, 8.9% in 2010-2011).Consequently, the total proportion receiving RT within 24 months of RP increased from 14. 1% in 2003-2004 to 19.8% in 2010-2011 (p < 0.0001).Conclusions: There was an increase in access to early RO referral post-RP and in ART utilization in Ontario from 2003 to 2012, following publication of key clinical trials and guidelines.

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)0.005
metaresearch head score (Gemma)0.023
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 score0.976
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.019
GPT teacher head0.297
Teacher spread0.278 · 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
Published2016
Admission routes1
Has abstractno

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