Wykorzystanie potencjału naukowo-badawczego i leczniczego oraz kondycja finansowa Narodowego Instytutu Onkologii im. Marii Skłodowskiej-Curie w latach 2017–2021
Bibliographic record
Abstract
Purpose – The study concerns the National Institute of Oncology, which is the largest medical research institute in Poland. It presents the development of the institution and evaluates the scientific, research and treatment activities as well as the financial situation of the above‑mentioned institutions. Research method – The article is a case study. The method of examining documents and data contained in the annual activity reports of the Director of the NIO and its financial statements for the years 2017–2021 has been used. In evaluation the Institute, the indicators for public healthcare entities included in the regulation of the Minister of Health has been applied. Results – The National Institute of Oncology conducts extensive research and develops innovative treatment methods that are practically used in the diagnosis and treatment of cancer. He actively participates in a task of exceptional importance for improving public health in Poland, i.e. the National Oncology Strategy. The difficult financial situation of the entity is gradually improving. Originality/value/implications/recommendations – The issue of medical research institutes is not handled in the literature on public health or public finance. Therefore, this publication fills the existing gap to some extent and is part of the series of articles devoted to the above‑mentioned issues, implemented by the authors. entities.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".