Reforzando las capacidades en investigación en informática para la salud global en la región andina a través de la colaboración internacional
Bibliographic record
Abstract
To improve global health and the welfare of a population, skilled human resources are required, not only in medicine and health, but also in the field of informatics. Unfortunately, training and research programs specific to biomedical informatics in developing countries are both scarce and poorly documented. The aim of this paper is to report the results from the first Informatics Expert Meeting for the Andean Region, including, nine Latin American based institutional case studies. This two-day event occurred in March 2010 and brought together twenty-three leaders in biomedical informatics from around the world. The blend of practical and experiential advice from these experts contributed to rich discussions addressing both challenges and applications of informatics within Latin American. In addition, to address the needs emphasized at the meeting, the QUIPU Network was established to expand the research consortium in the Andean Region, Latin America, and internationally. The use of these new technologies in existing public health training and research programs will be key to improving the health of populations in the Andean Region and around the globe.
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 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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".