Identification and selection of health technologies for assessment by agencies in support of reimbursement decisions in Latin America
Bibliographic record
Abstract
OBJECTIVE: There is no health system that has the resources to evaluate all technologies. The presence of a clear process to prioritize health technologies for assessment by health technology assessment (HTA) agencies is a good practice principle recognized at the international level. The objective of Health Technology Assessment International's 2020 Latin American Policy Forum (LatamPF) was to explore how to improve the way HTA agencies in Latin America identify and prioritize technologies for assessment. METHODS: This paper is based on a background document, a survey, and the deliberations of the members of the LatamPF (forty-six participants from eleven countries) using a design thinking methodology. RESULTS: Participants agreed that a lack of clear prioritization mechanisms results in HTA processes and decisions that are perceived to be of low transparency and overly exposed to political or interest group pressures. The LatamPF identified barriers and recommended actions to improve HTA prioritization mechanisms in Latin America. The criteria identified as the most important to be taken into consideration by HTA agencies in the region when prioritizing a technology for assessment were: the burden of illness, the potential clinical benefit, the alignment with national health priorities, the potential impact on equity, a lack of treatment alternatives for patients, and the potential economic impact. CONCLUSIONS: Forum participants agreed that the establishment of transparent prioritization processes is a key element for all health systems. Improvements in these processes will strengthen HTA and provide greater legitimacy to decision making.
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 | 0.090 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".