Navigating the future: horizon scanning and early dialogue in health technology assessment in Latin America
Bibliographic record
Abstract
OBJECTIVE: To systematize the information and perspectives shared during the 2024 LATAM policy forum, which explored advancements in horizon scanning and early dialogue processes in the region, by analyzing the main discussion and identifying the main lessons. METHODS: This article is based on the discussions and background materials provided during the 1.5 days in-person 2024 Latin American Policy Forum (59 representatives from 11 countries). We gathered and systematized the information shared during the forum, including the results of a pre-forum survey. The Forum agenda included keynote presentations, breakout group activities, and plenary discussions to identify the main lessons and key messages from all different stakeholders' points of view. RESULTS: The forum highlighted the growing recognition of the need for structured horizon scanning and early dialogue processes in Latin America. Key barriers were identified, including the absence of clear legal frameworks, limited data availability, and the need for capacity-building. Potential solutions included fostering regional cooperation, improving transparency, and creating pilot programs for early engagement. Engaging patients and the pharmaceutical industry was deemed essential for trust and foster alignment between HTA agencies and regulators. CONCLUSIONS: Horizon scanning and early dialogue represent critical tools for improving health system preparedness and aligning innovation with local needs. Their implementation, however, requires coordinated efforts across multiple stakeholders, enhanced dialogue, and the development of supportive legal and regulatory frameworks.
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 | 0.143 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".