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Record W2025771461 · doi:10.1016/j.jval.2014.03.1100

Evaluation of current data sources in Europe for the conduct of real-world studies on lung and renal cell carcinoma: A systematic literature review

2014· article· en· W2025771461 on OpenAlexaff
Inna Gridchyna, M Manley Daumont, Alia Yousif, Ishan Hirji, M Lees, Francesca Di Salvo, Yola Moride

Bibliographic record

VenueValue in Health · 2014
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineLung cancerRenal cell carcinomaCancerReal world dataPopulationKidney cancerClinical trialMEDLINECancer registryCohortOncologySystematic reviewInternal medicineIntensive care medicineData scienceComputer scienceEnvironmental health

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.156
metaresearch head score (Gemma)0.430
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.827

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1560.430
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0140.016
Bibliometrics0.0370.035
Science and technology studies0.0010.003
Scholarly communication0.0090.007
Open science0.0050.007
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.257
GPT teacher head0.478
Teacher spread0.221 · 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 designSystematic review
DomainMethods
GenreReview

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
Published2014
Admission routes1
Has abstractno

Explore more

Same venueValue in Health→Same topicLung Cancer Treatments and Mutations→French-language works237,207→