PHARMACOECONOMIC COMPONENT OF A CLINICAL TRIAL CONDUCTED IN LATIN AMERICA
Bibliographic record
Abstract
BACKGROUND: Although pharmacoeconomic studies constitute a valuable tool for better managing drug consumption, the conditions under which such studies would be performed in Latin American countries have not been explored. OBJECTIVES: The aim of this paper is to evaluate the potential advantages of and pitfalls in doing pharmacoeconomic research in Latin America and to propose avenues to facilitate the development of this field in the region. METHODS: The Canadian guidelines for the economic evaluation of pharmaceuticals served as a structured framework to assess, both prospectively and retrospectively, the conditions under which the pharmacoeconomic component of a clinical trial held in Mexico and Brazil would be and actually was conducted. RESULTS: The conditions under which pharmacoeconomic evaluations are conducted must be improved if studies are to contribute to the better management of scarce resources across the entire health care system. CONCLUSIONS: The creation of a public funding agency, the reappraisal of administrative data as a management tool in both the public and the private sectors, and the establishment of national guidelines should be considered within the framework of reforms aimed at allowing healthcare systems to meet their objectives of efficiency and equity.
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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 | 0.110 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".