The paradox of the quality of medical care in the system of compulsory medical insurance within the framework of modern purchases
Bibliographic record
Abstract
The study aims to assess the impact of restrictions on the purchases of pharmaceuticals that are outside the list of vital and essential drugs (VED) on the availability and quality of medical care for cancer patients in the system of compulsory medical insurance (CMI). Materials and methods. A sociological study was conducted in 2024–2025 using a telephone and online survey of 502 patients with cancer from various regions of the Russian Federation. In addition, it analyzed appeals to the hotline of the Interregional public movement (IPM) “Movement against cancer” and the purchase of drugs that are not included in the VED list for 2024 and 2025. Results. 34.5 % of patients had problems with receiving anticancer therapy, 24.1% - with receiving drugs in the hospital. 70 % of respondents had a need to purchase drugs at their own expense. Among patients who were prescribed therapy with drugs outside the VED list (n = 73), 80 % experienced problems receiving it. During 9 months of 2025, the number of requests for drugs outside the VED list increased by 5 % as compared with the same period in 2024. It was established that individual purchases are associated with long approval periods (from several weeks to months), which is critical for cancer patients. The use of innovative analgesics outside the VED list (for example, tyrosyl-D-arginyl-phenylalanyl-glycine amide) can increase the effectiveness of treatment and reduce the costs of CMI. Conclusion. The current mechanism for purchasing drugs outside the VED list does not provide timely access for patients to the necessary therapy. It is recommended to develop a system of predicted provision of such drugs to improve the quality and accessibility of cancer care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".