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Record W1968478818 · doi:10.3747/co.20.1351

New Chart Review Data Validate Administrative Data–Based Indicator for Guideline-Recommended Treatment of Locally Advanced Non-Small-Cell Lung Cancer and Shed Light on Reasons for Non-Referral and Non-Treatment

2013· article· en· W1968478818 on OpenAlexaffvenueabout
Julie Klein-Geltink, Tonia Forte, Rami Rahal, Gail Darling, Winson Y. Cheung, R. Alvi, Glen Noonan, Cynthia L. Russell, K.A. Vriends, Jin Niu, Gina Lockwood, Heather Bryant

Bibliographic record

VenueCurrent Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of CalgaryPublic Health OntarioAlberta Health ServicesBC Cancer AgencyCancerCare ManitobaSaskatchewan Cancer AgencyUniversity of TorontoToronto General HospitalUniversity Health NetworkCanadian Partnership Against Cancer
Fundersnot available
KeywordsMedicineGuidelineReferralGeneral partnershipCancerLung cancerChartFamily medicineOncologyPathologyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

The 2012 Cancer System Performance Report is the 4th annual report on the Canadian cancer control system produced by the System Performance initiative at the Canadian Partnership Against Cancer, in collaboration with its provincial and national partners. [...]

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)0.024
metaresearch head score (Gemma)0.160
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.160
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.015
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.182
GPT teacher head0.477
Teacher spread0.295 · 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.

Study designObservational
DomainEvaluation
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

Citations1
Published2013
Admission routes3
Has abstractyes

Explore more

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