The real practice of clinical and economic research of drugs included in the Federal Program of High-Cost Nosologies
Bibliographic record
Abstract
Objective: to assess the compliance of the actual practice of conducting clinical and economic research with the requirements applicable in the Russian Federation (RF) when including drugs in the Federal Program of High-Cost Nosologies (HCN). Material and methods. In the CyberLeninka and eLibrary databases, a search was made for clinical and economic studies of medicines included in the HCN list published in the RF in the period from 2011 to June 2021. Results. Information was obtained on 23 published clinical and economic studies of the effectiveness of drugs, which is less than 30% of all drugs included in the HCN program during the specified period. More than half of the studies of chronic disabling diseases had a modeling horizon of 1 year. The sensitivity analysis of the results in over 1/3 of cases considered only the deviation of the price of the strategies under consideration, and in a quarter of cases it was not carried out at all. Only 4 studies evaluated the increase in quality-adjusted life year, although, for chronic disabling diseases, quality of life is one of the key performance indicators. Conclusion. In the RF, less than 30% of the results of pharmacoeconomical studies of drugs included in the HCN Program are published, which does not allow to make adequate evaluation of pharmacoeconomical approaches to its formation. To analyze the effectiveness of the tools used in assessing the economic efficiency of expensive medical technologies, a further retrospective research of the studies conducted in the RF is required.
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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 | 0.297 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".