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Record W2492220089 · doi:10.21037/apm.2016.06.03

The power of integration: radiotherapy and global palliative care

2016· review· en· W2492220089 on OpenAlexaff
Danielle Rodin, Surbhi Grover, Shekinah N.C. Elmore, Felícia Marie Knaul, Rifat Atun, Lisa Caulley, Cristián Herrera, Joshua Jones, Aryeh J. Price, Anusheel Munshi, Ajeet Kumar Gandhi, Chiman Shah, Mary Gospodarowicz

Bibliographic record

VenueAnnals of Palliative Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity of Ottawa
Fundersnot available
KeywordsMedicinePalliative careCapacity buildingGlobal healthLow and middle income countriesScale (ratio)Developing countryNursingEconomic growthPublic health

Abstract

fetched live from OpenAlex

Radiotherapy (RT) is a powerful tool for the palliation of the symptoms of advanced cancer, although access to it is limited or absent in many low- and middle-income countries (LMICs). There are multiple factors contributing to this, including assumptions about the economic feasibility of RT in LMICs, the logical challenges of building capacity to deliver it in those regions, and the lack of political support to drive change of this kind. It is encouraging that the problem of RT access has begun to be included in the global discourse on cancer control and that palliative care and RT have been incorporated into national cancer control plans in some LMICs. Further, RT twinning programs involving high- and low-resource settings have been established to improve knowledge transfer and exchange. However, without large-scale action, the consequences of limited access to RT in LMICs will become dire. The number of new cancer cases around the world is expected to double by 2030, with twice as many deaths occurring in LMICs as in high-income countries (HICs). A sustained and coordinated effort involving research, education, and advocacy is required to engage global institutions, universities, health care providers, policymakers, and private industry in the urgent need to build RT capacity and delivery in LMICs.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.097
GPT teacher head0.504
Teacher spread0.407 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations11
Published2016
Admission routes1
Has abstractyes

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