Asociación de Hemato-Oncología Pediátrica de Centro América (AHOPCA): A model for sustainable development in pediatric oncology
Bibliographic record
Abstract
Bridging the survival gap for children with cancer, between those (the great majority) in low and middle income countries (LMIC) and their economically advantaged counterparts, is a challenge that has been addressed by twinning institutions in high income countries with centers in LMIC. The long-established partnership between a Central American consortium--Asociación de Hemato-Oncología Pediátrica de Centro América (AHOPCA)--and institutions in Europe and North America provides a striking example of such a twinning program. The demonstrable success of this endeavor offers a model for improving the health outcomes of children with cancer worldwide. As this remarkable enterprise celebrates its 15th anniversary, it is appropriate to reflect on its origin, subsequent growth and development, and the lessons it provides for others embarking on or already engaged in similar journeys. Many challenges have been encountered and not all yet overcome. Commitment to the endeavor, collaboration in its achievements and determination to overcome obstacles collectively are the hallmarks that stamp AHOPCA as a particularly successful partnership in advancing pediatric oncology in the developing world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".