MétaCan
Menu
Back to cohort
Record W80140506 · doi:10.1177/030089160909500504

Comprehensive cancer control-research & development: knowing what we do and doing what we know

2009· article· en· W80140506 on OpenAlexafffund
Jon Kerner, Eduardo Cazap, Derek Yach, Marco A. Pierotti, Maria Grazia Daidone, Pasquale De Blasio, Peter Geary, Brent Schacter, Milena Sant, J. Dik F. Habbema, Rengaswamy Sankaranarayanan, Catherine G. Sutcliffe, Simon Sutcliffe

Bibliographic record

VenueTumori Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsCancerCare ManitobaGenome CanadaCanadian Partnership Against Cancer
FundersPartenariat Canadien Contre Le CancerMinistério da SaúdeChinese Academy of Medical SciencesAlleanza Contro il CancroInternational Atomic Energy AgencyPublic Health AgencyEuropean School of OncologyRegione LombardiaAcademy of Medical SciencesNational Cancer InstitutePublic Health Agency of Canada
KeywordsPrivate sectorPublic sectorBusinessContext (archaeology)Public relationsPsychological interventionPopulationPublic healthInvestment (military)Economic growthPublic economicsPolitical scienceMedicineEconomicsEnvironmental healthNursingPolitics

Abstract

fetched live from OpenAlex

Comprehensive cancer control is defined as an integrated and coordinated approach to reducing cancer incidence, morbidity, and mortality across the cancer control continuum from primary prevention to end-of-life care. This approach assumes that when the public sector, non-governmental organizations, academia, and the private sector share with each other their skills, knowledge, and resources, a country can take advantage of all its talents and resources to more quickly reduce the burden of cancer for all its population. One critical issue for comprehensive cancer control is the extent to which the private sector can contribute to cancer prevention and control programs and policies that have historically been lead by the public health sector, and similarly how can the public sector increase its investment and involvement in clinical research and practice issues that are largely driven by the private sector worldwide? In addition, building capacity to integrate research that is appropriate to the culture and context of the population will be important in different settings, in particular research related to cancer control interventions that have the capacity to influence outcomes. To whatever extent cancer control research is ultimately funded through the private and public sectors, if investments in research discoveries are ultimately to benefit the populations that bear the greatest burden of disease, then new approaches to integrating the lessons learned from science with the lessons learned from service (public health, clinical, and public policy) must be found to close the gap between what we know and what we do. Communities of practice for international cancer control, like the ones fostered by the first three International Cancer Control Congresses, represent an important forum for knowledge exchange opportunities to accelerate the translation of new knowledge into action to reduce the burden of cancer worldwide.

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)0.082
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.918
Threshold uncertainty score0.431

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.094
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.006
Science and technology studies0.0100.048
Scholarly communication0.0310.037
Open science0.0040.019
Research integrity0.0150.034
Insufficient payload (model declined to judge)0.0110.006

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 head0.145
GPT teacher head0.418
Teacher spread0.273 · 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 designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations7
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueTumori JournalSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207