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Record W2771912865 · doi:10.1177/0840470417729171

Building the Ontario Surgical Oncology Program

2017· article· en· W2771912865 on OpenAlexaffabout
Terrence Sullivan, Jonathan C. Irish

Bibliographic record

VenueHealthcare Management Forum · 2017
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRadiation oncologyMedicineQuality (philosophy)CancerBest practiceOncologyNursingFamily medicineBusinessRadiation therapyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

From the 1940s onward the establishment of an increasing number of cancer centres in Ontario concentrated predominantly on radiation and systemic therapy. Increasing attention to the organization of cancer surgery services in Ontario over the last 3 decades through measures led by Cancer Care Ontario has resulted in building a progressive provincial and regional surgical oncology network. These networks are rooted in leadership mobilization and have used quality data and best evidence to promote the elaboration of communities of practice to achieve better outcomes. This article briefly chronicles this journey and points to areas of progress.

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)0.009
metaresearch head score (Gemma)0.011
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 score0.931
Threshold uncertainty score0.504

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0130.005
Scholarly communication0.0050.003
Open science0.0020.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0350.006

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 head0.036
GPT teacher head0.456
Teacher spread0.420 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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