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Record W2050949393 · doi:10.1002/pbc.20280

AMOR: A proposed cooperative effort to improve outcomes of childhood cancer in Central America

2005· article· en· W2050949393 on OpenAlexaff
Federico Antillón, Fulgencio Báez, Ronald D. Barr, Jose C. Barrantes Zamorra, Ligia Fu Carrasco, Bélgica Moreno, Miguel Bonilla, Gianni Tognoni, Maria G. Valsecch, Scott C. Howard, Raul C. Ribeiro, Giuseppe Masera

Bibliographic record

VenuePediatric Blood & Cancer · 2005
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsMcMaster Children's Hospital
Fundersnot available
KeywordsMedicineGeneral partnershipChildhood cancerPediatric oncologyDeveloping countryCancerPediatric cancerFamily medicineHealth careEconomic growthInternal medicinePolitical science

Abstract

fetched live from OpenAlex

The dramatic reduction of pediatric cancer mortality rates has been one of the greatest accomplishments of contemporary medicine. About 80% of children with cancer are now expected to be cured by current therapies. However, most of the world's children have no access to cancer treatment. The translation of effective pediatric cancer therapies to impoverished regions of the world presents an enormous challenge to the health care profession. Over the past 20 years, efforts have been under way to extend adequate cancer treatment to an increasing number of children in developing countries. These initiatives, collectively designated "twinning programs," consist essentially of a partnership between a pediatric cancer unit in a developing country and a group of health care providers in the developed world. Here we review the twinning programs that have been implemented in Central America, discuss their impact on the development of local resources and the outcome of childhood cancer, and propose a collaborative research initiative aimed at improving the international dissemination of progress in pediatric hematology-oncology.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.301
Teacher spread0.291 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations42
Published2005
Admission routes1
Has abstractyes

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