Bibliographic record
Abstract
This paper provides an updated review of the dengue situation in the Latin-American countries, focusing on Colombia as a highly and historically affected nation. In first instance, it presents a scientific overview about the biology, clinical progress, transmission mode and epidemiology of the disease. Secondly, it describes the different outbreaks in the region during the past five decades. Thirdly, as an illustration of historical trends in most provinces and Colombian territories, early detection and predictive value of a dengue epidemic is inadequate and how a surveillance system should work. Based on these, the document proposes to provide a framework for a pilot model of a sustained and integrated epidemiological surveillance system in Colombia, focused on early detection, prediction (turning point) of outbreaks and recommendation of a model to be implemented by the Colombian local health units of each affected territory. It emphasizes that a vector-borne disease such as dengue must be addressed locally, having the individual, all the way through a primary health care and population-based approach, at the center of the system. It also highlights a new approach for returning to the basics in primary health attention in which the community health promoter be again one of the main participants. The paper concludes that without a primary health care approach, particularly under the existing decentralized local and regional governments, it will be so difficult to achieve real control over this kind of cyclical public health issues.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".