[Establishing an insight-gaining area in pharmacoeconomics].
Bibliographic record
Abstract
INTRODUCTION: The use of economic evaluation methods is very important in health care, in order to select those medicines that could be funded by National Health Services, or that could be included in the hospital or primary health care formularies. The development of the Clearinghouse on Pharmacoeconomics, available for health care professionals, represents an important advance for the information-knowledge system which is basic for professionals to understand how important it is to incorporate efficiency into decision making. Both primary health care and hospitals, need to assure the quality of pharmacoeconomic information that must be taken into account when evaluating the use of new or in use drugs. OBJECTIVES: To establish a Clearinghouse on Pharmacoeconomics with the most important resources on this subject available in Internet from the countries of the European Union, United States and Canada. METHODS: Descriptive study of web sites of institutions and centers and other pharmacoeconomics and health economics resources from 24 OECD countries mainly from the European Union, United States and Canada. RESULTS: The Clearinghouse of Pharmacoeconomics has identified information from 55 centers of pharmacoeconomics and health economics, 20 journals, 32 books, 19 data bases of medicines, 14 periodic bulletins of pharmacoeconomics subjects, and 16 professional associations. It also includes articles, two-monthly bulletins and health news which can all be found in a web site under the denomination of Clearinghouse on Pharmacoeconomics. The number of visits has been of 8,488 since June 30th of the year 2000. CONCLUSIONS: A Clearinghouse on Pharmacoeconomics that includes the most important resources from the countries in the European Union, the United States and Canada related to pharmacoeconomics and health economics has been established. The number of visits from June 2000 to May 2003 was 8,488. To establish, increase and maintain the number of users who visit and obtain data from the clearinghouse on pharmacoeconomics, the use of an alert system every fifteen days has proved effective.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".