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Record W4405535660 · doi:10.1016/s1470-2045(24)00522-9

Integrating cancer into crisis: a global vision for action from WHO and partners

2024· review· en· W4405535660 on OpenAlexaff
Raffaella Casolino, Richard Sullivan, Kiran Jobanputra, May Abdel–Wahab, Milica Ljaljević Grbić, Nazik Hammad, Tezer Kutluk, Nelya Melnitchouk, Alexandra S. Mueller, Roberta Ortiz, Diana Páez, Omar Shamieh, Gevorg Tamamyan, Horia Vulpe, Bente Mikkelsen, Andrè Ilbawi, Slim Slama

Bibliographic record

VenueThe Lancet Oncology · 2024
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersOpenAIWorld Health Organization
KeywordsAction (physics)CancerPolitical scienceMedicine

Abstract

fetched live from OpenAlex

More than a billion people live in fragile, conflict-affected, and vulnerable settings requiring humanitarian support, where cancer is a substantial health issue. Despite its substantial effect on populations, cancer care remains underprioritised in emergency preparedness and response frameworks and humanitarian operational planning. This Policy Review summarises the perspectives and actionable recommendations from the First Global High-Level Technical Meeting on Non-communicable Diseases in Humanitarian Settings, with a focus on cancer. The paper highlights the challenges of providing cancer care in fragile, conflict-affected, and vulnerable settings and proposes a comprehensive roadmap to address both immediate and long-term needs of patients with cancer living in these settings. Key solutions include: integrating the cancer care continuum into national preparedness and response plans to enhance health-care system resilience; integrating cancer into humanitarian responses efforts; addressing the specific needs of paediatric patients with cancer; improving cancer intelligence and surveillance systems; and developing strategies to navigate the logistical and financial challenges of providing cancer care during crises. Additionally, the paper outlines practical actions and next steps for international cooperation needed to drive a shift in global health priorities and elevate cancer in the global health security agenda. We hope the presented notions will help prevent millions of avoidable deaths among people with cancer.

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 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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.695
GPT teacher head0.635
Teacher spread0.059 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
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

Citations33
Published2024
Admission routes1
Has abstractyes

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