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Record W4307033841 · doi:10.1177/10732748221119354

An International Consensus on Actions to Improve Lung Cancer Survival: A Modified Delphi Method Among Clinical Experts in the International Cancer Benchmarking Partnership

2022· article· en· W4307033841 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCancer Control · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsMedicineBenchmarkingLung cancerGeneral partnershipDelphi methodCancerDelphiOncologyInternal medicineArtificial intelligenceManagement

Abstract

fetched live from OpenAlex

BACKGROUND: Research from the International Cancer Benchmarking Partnership (ICBP) demonstrates that international variation in lung cancer survival persists, particularly within early stage disease. There is a lack of international consensus on the critical contributing components to variation in lung cancer outcomes and the steps needed to optimise lung cancer services. These are needed to improve the quality of options for and equitable access to treatment, and ultimately improve survival. METHODS: Semi-structured interviews were conducted with 9 key informants from ICBP countries. An international clinical network representing 6 ICBP countries (Australia, Canada, Denmark, England, Ireland, New Zealand, Northern Ireland, Scotland & Wales) was established to share local clinical insights and examples of best practice. Using a modified Delphi consensus model, network members suggested and rated recommendations to optimise the management of lung cancer. Calls to Action were developed via Delphi voting as the most crucial recommendations, with Good Practice Points included to support their implementation. RESULTS: Five Calls to Action and thirteen Good Practice Points applicable to high income, comparable countries were developed and achieved 100% consensus. Calls to Action include (1) Implement cost-effective, clinically efficacious, and equitable lung cancer screening initiatives; (2) Ensure diagnosis of lung cancer within 30 days of referral; (3) Develop Thoracic Centres of Excellence; (4) Undertake an international audit of lung cancer care; and (5) Recognise improvements in lung cancer care and outcomes as a priority in cancer policy. CONCLUSION: The recommendations presented are the voice of an expert international lung cancer clinical network, and signpost key considerations for policymakers in countries within the ICBP but also in other comparable high-income countries. These define a roadmap to help align and focus efforts in improving outcomes and management of lung cancer patients globally.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.307
GPT teacher head0.582
Teacher spread0.276 · 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